{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-12-11T09:31:56.188789Z",
     "start_time": "2024-12-11T09:31:53.686038Z"
    }
   },
   "source": [
    "import os\n",
    "\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data import DataLoader\n",
    "from ResNet import resnet50  # 从自定义的ResNet.py文件中导入resnet50这个函数\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from utils import read_split_data, train_one_epoch, evaluate\n",
    "from my_dataset import MyDataSet"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-11T09:31:57.158672Z",
     "start_time": "2024-12-11T09:31:56.200455Z"
    }
   },
   "cell_type": "code",
   "source": "torch.cuda.is_available()",
   "id": "d917bd6273572a9a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-11T09:31:57.239975Z",
     "start_time": "2024-12-11T09:31:57.234362Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# -------------------------------------------------- #\n",
    "# （0）参数设置\n",
    "# -------------------------------------------------- #\n",
    "batch_size = 256   # 每个step训练32张图片\n",
    "epochs = 20  # 共训练10次\n",
    "\n",
    "# -------------------------------------------------- #\n",
    "# （1）文件配置\n",
    "# -------------------------------------------------- #\n",
    "# 数据集文件夹位置\n",
    "filepath = r'./input'\n",
    "# 权重文件位置\n",
    "weightpath = './input/pretrained_weights/resnet50.pth'\n",
    "# 权重保存文件夹路径\n",
    "savepath = './input/save_weights/'\n",
    "\n",
    "# 获取GPU设备\n",
    "if torch.cuda.is_available():  # 如果有GPU就用，没有就用CPU\n",
    "    device = torch.device('cuda:0')\n",
    "else:\n",
    "    device = torch.device('cpu')"
   ],
   "id": "24e4fed2456f51a9",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-11T09:31:57.549516Z",
     "start_time": "2024-12-11T09:31:57.299070Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# -------------------------------------------------- #\n",
    "# （2）构造数据集\n",
    "# -------------------------------------------------- #\n",
    "# 训练集的数据预处理\n",
    "transform_train = transforms.Compose([\n",
    "    # # 数据增强，随机裁剪224*224大小\n",
    "    # transforms.RandomResizedCrop(224),\n",
    "    # transforms.Resize((224, 224)),\n",
    "    # transforms.Resize((32, 32)),\n",
    "    transforms.Resize((26, 26)),\n",
    "    transforms.RandomRotation(45), #随机旋转， -45到45度之间随机选择\n",
    "    transforms.RandomHorizontalFlip(p=0.5), #随机水平翻转，选择一个概率\n",
    "    transforms.RandomVerticalFlip(p=0.5), #随机垂直翻转\n",
    "    # 数据变成tensor类型，像素值归一化，调整维度[h,w,c]==>[c,h,w]\n",
    "    transforms.ToTensor(),\n",
    "    # 对每个通道的像素进行标准化，给出每个通道的均值和方差\n",
    "    transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])\n",
    "\n",
    "# 验证集的数据预处理\n",
    "transform_val = transforms.Compose([\n",
    "    # 将输入图像大小调整为224*224\n",
    "    # transforms.Resize((224, 224)),\n",
    "    # transforms.Resize((32, 32)),\n",
    "    transforms.Resize((26, 26)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])\n",
    "\n",
    "# # 读取训练集并预处理\n",
    "# train_dataset = datasets.ImageFolder(root=filepath + 'train',  # 训练集图片所在的文件夹\n",
    "#                                      transform=transform_train)  # 训练集的预处理方法\n",
    "#\n",
    "# # 读取验证集并预处理\n",
    "# val_dataset = datasets.ImageFolder(root=filepath + 'val',  # 验证集图片所在的文件夹\n",
    "#                                    transform=transform_val)  # 验证集的预处理方法\n",
    "\n",
    "train_images_path, train_images_label, val_images_path, val_images_label = read_split_data(filepath +r''+os.sep+'train')\n",
    "# 实例化训练数据集\n",
    "train_dataset = MyDataSet(images_path=train_images_path,\n",
    "                          images_class=train_images_label,\n",
    "                          transform=transform_train)\n",
    "\n",
    "# 实例化验证数据集\n",
    "val_dataset = MyDataSet(images_path=val_images_path,\n",
    "                        images_class=val_images_label,\n",
    "                        transform=transform_val)\n",
    "# 查看训练集和验证集的图片数量\n",
    "train_num = len(train_dataset)\n",
    "val_num = len(val_dataset)\n",
    "print('train_num:', train_num, 'val_num:', val_num)  # 453, 112\n",
    "\n",
    "# 查看图像类别及其对应的索引\n",
    "class_dict = {'Center':0,'Donut':1,'Edge-Loc':2,'Edge-Ring':3,'Loc':4,'Near-full':5,'Random':6,'Scratch':7}\n",
    "print(class_dict)\n",
    "# 将类别名称保存在列表中\n",
    "class_names = list(class_dict.keys())"
   ],
   "id": "340b6d388ba8cc3e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17625 images were found in the dataset.\n",
      "14104 images for training.\n",
      "3521 images for validation.\n",
      "train_num: 14104 val_num: 3521\n",
      "{'Center': 0, 'Donut': 1, 'Edge-Loc': 2, 'Edge-Ring': 3, 'Loc': 4, 'Near-full': 5, 'Random': 6, 'Scratch': 7}\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-11T09:31:57.567451Z",
     "start_time": "2024-12-11T09:31:57.561727Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 构造训练集\n",
    "train_loader = DataLoader(dataset=train_dataset,  # 接收训练集\n",
    "                          batch_size=batch_size,  # 训练时每个step处理32张图\n",
    "                          shuffle=True,  # 打乱每个batch\n",
    "                          num_workers=0)  # 加载数据时的线程数量，windows环境下只能=0\n",
    "\n",
    "# 构造验证集\n",
    "val_loader = DataLoader(dataset=val_dataset,\n",
    "                        batch_size=batch_size,\n",
    "                        shuffle=False,\n",
    "                        num_workers=0)"
   ],
   "id": "240448c3a63f5b3b",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-11T09:31:57.756565Z",
     "start_time": "2024-12-11T09:31:57.586259Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# -------------------------------------------------- #\n",
    "# （3）数据可视化\n",
    "# -------------------------------------------------- #\n",
    "# 取出一个batch的训练集，返回图片及其标签\n",
    "train_img, train_label = next(iter(train_loader))\n",
    "# 查看shape, img=[32,3,224,224], label=[32]\n",
    "print(train_img.shape, train_label.shape)"
   ],
   "id": "62e099f355fc9ce8",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([256, 3, 26, 26]) torch.Size([256])\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-11T09:31:57.995963Z",
     "start_time": "2024-12-11T09:31:57.814577Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 从一个batch中取出前9张图片\n",
    "img = train_img[:9]  # [9, 3, 224, 224]\n",
    "# 将图片反标准化，像素变到0-1之间\n",
    "img = img / 2 + 0.5\n",
    "# tensor类型变成numpy类型\n",
    "img = img.numpy()\n",
    "class_label = train_label.numpy()\n",
    "# 维度重排 [b,c,h,w]==>[b,h,w,c]\n",
    "img = np.transpose(img, [0, 2, 3, 1])\n",
    "\n",
    "# 创建画板\n",
    "plt.figure()\n",
    "# 绘制四张图片\n",
    "for i in range(img.shape[0]):\n",
    "    plt.subplot(3, 3, i + 1)\n",
    "    plt.imshow(img[i])\n",
    "    plt.xticks([])  # 不显示x轴刻度\n",
    "    plt.yticks([])  # 不显示y轴刻度\n",
    "    plt.title(class_names[class_label[i]])  # 图片对应的类别\n",
    "\n",
    "plt.tight_layout()  # 轻量化布局\n",
    "plt.show()"
   ],
   "id": "b0e18059b1fe98e1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-11T09:31:59.530452Z",
     "start_time": "2024-12-11T09:31:58.010390Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "# -------------------------------------------------- #\n",
    "# （4）加载模型\n",
    "# -------------------------------------------------- #\n",
    "# 8分类层\n",
    "net = resnet50(num_classes=8, include_top=True)\n",
    "pre_weights = torch.load(weightpath,weights_only=True)\n",
    "net.to(device)\n",
    "\n",
    "# 为网络重写分类层\n",
    "in_channel = net.fc.in_features  # 2048\n",
    "net.fc = nn.Linear(in_channel, 8)  # [b,2048]==>[b,4]\n",
    "# 将模型搬运到GPU上\n",
    "net.to(device)"
   ],
   "id": "2ff180ea5355d7da",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (4): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (5): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=2048, out_features=8, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-11T09:31:59.876744Z",
     "start_time": "2024-12-11T09:31:59.869637Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 定义交叉熵损失\n",
    "loss_function = nn.CrossEntropyLoss()\n",
    "\n",
    "# 定义优化器\n",
    "optimizer = optim.Adam(net.parameters(), lr=0.001)\n",
    "# scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)\n",
    "\n",
    "# 保存准确率最高的一次迭代\n",
    "best_acc = 0.0\n",
    "losses_train = []\n",
    "accuracy_train = []\n",
    "losses_val = []\n",
    "accuracy_val = []"
   ],
   "id": "6b4d0128130c99aa",
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-11T09:36:03.779566Z",
     "start_time": "2024-12-11T09:32:00.514513Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# -------------------------------------------------- #\n",
    "# （5）网络训练\n",
    "# -------------------------------------------------- #\n",
    "for epoch in range(epochs):\n",
    "\n",
    "    print('-' * 30, '\\n', 'epoch:', epoch)\n",
    "\n",
    "    # 将模型设置为训练模型, dropout层和BN层只在训练时起作用\n",
    "    net.train()\n",
    "\n",
    "    # 计算训练一个epoch的总损失\n",
    "    running_loss = 0.0\n",
    "    train_correct = 0.0\n",
    "    train_total = 0.0\n",
    "    # scheduler.step()\n",
    "    # 每个step训练一个batch\n",
    "    for step, data in enumerate(train_loader):\n",
    "        # data中包含图像及其对应的标签\n",
    "        images, labels = data\n",
    "\n",
    "        # 梯度清零，因为每次计算梯度是一个累加\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # 前向传播\n",
    "        outputs = net(images.to(device))\n",
    "\n",
    "        # 计算预测值和真实值的交叉熵损失\n",
    "        loss = loss_function(outputs, labels.to(device))\n",
    "        predicted = torch.argmax(outputs, 1)\n",
    "        train_correct += (predicted == labels.to(device)).sum().item()\n",
    "        train_total += labels.size(0)\n",
    "        # 梯度计算\n",
    "        loss.backward()\n",
    "\n",
    "        # 权重更新\n",
    "        optimizer.step()\n",
    "\n",
    "        # 累加每个step的损失\n",
    "        running_loss += loss.item()\n",
    "\n",
    "        # # 打印每个step的损失\n",
    "        # print(f'step:{step} loss:{loss}')\n",
    "    train_loss = running_loss / step\n",
    "    train_accuracy = train_correct / train_total\n",
    "    losses_train.append(train_loss)\n",
    "    accuracy_train.append(train_accuracy)\n",
    "    # -------------------------------------------------- #\n",
    "    # （6）网络验证\n",
    "    # -------------------------------------------------- #\n",
    "    net.eval()  # 切换为验证模型，BN和Dropout不起作用\n",
    "\n",
    "    acc = 0.0  # 验证集准确率\n",
    "    valid_loss = 0.0\n",
    "    with torch.no_grad():  # 下面不进行梯度计算\n",
    "\n",
    "        # 每次验证一个batch\n",
    "        for data_test in val_loader:\n",
    "            # 获取验证集的图片和标签\n",
    "            test_images, test_labels = data_test\n",
    "\n",
    "            # 前向传播\n",
    "            outputs = net(test_images.to(device))\n",
    "\n",
    "            # 预测分数的最大值\n",
    "            predict_y = torch.max(outputs, dim=1)[1]\n",
    "            loss_val = loss_function(outputs, test_labels.to(device))\n",
    "            valid_loss += loss_val.item()\n",
    "            # 累加每个step的准确率\n",
    "            acc += (predict_y == test_labels.to(device)).sum().item()\n",
    "\n",
    "        # 计算所有图片的平均准确率\n",
    "        acc_test = acc / val_num\n",
    "\n",
    "        # 打印每个epoch的训练损失和验证准确率\n",
    "        print(f'total_train_loss:{running_loss / step}, total_test_acc:{acc_test}')\n",
    "\n",
    "        # -------------------------------------------------- #\n",
    "        # （7）权重保存\n",
    "        # -------------------------------------------------- #\n",
    "        # 保存最好的准确率的权重\n",
    "        if acc_test > best_acc:\n",
    "            # 更新最佳的准确率\n",
    "            best_acc = acc_test\n",
    "            # 保存的权重名称\n",
    "            savename = savepath + 'resnet50_dataAug_epo100_class8.pth'\n",
    "            # 保存当前权重\n",
    "            torch.save(net.state_dict(), savename)\n",
    "        losses_val.append(valid_loss/len(val_loader))\n",
    "        accuracy_val.append(acc_test)\n",
    "print(best_acc)"
   ],
   "id": "4bdcbcf76cb0ef98",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------ \n",
      " epoch: 0\n",
      "total_train_loss:0.6826215673576702, total_test_acc:0.7671116160181767\n",
      "------------------------------ \n",
      " epoch: 1\n",
      "total_train_loss:0.41430698389356785, total_test_acc:0.9040045441635899\n",
      "------------------------------ \n",
      " epoch: 2\n",
      "total_train_loss:0.29190864346244116, total_test_acc:0.9162169838114173\n",
      "------------------------------ \n",
      " epoch: 3\n",
      "total_train_loss:0.2625820149074901, total_test_acc:0.8804316955410395\n",
      "------------------------------ \n",
      " epoch: 4\n",
      "total_train_loss:0.26003017777746373, total_test_acc:0.9167850042601534\n",
      "------------------------------ \n",
      " epoch: 5\n",
      "total_train_loss:0.20009850683537397, total_test_acc:0.9406418631070719\n",
      "------------------------------ \n",
      " epoch: 6\n",
      "total_train_loss:0.16881204464218832, total_test_acc:0.9505822209599546\n",
      "------------------------------ \n",
      " epoch: 7\n",
      "total_train_loss:0.17507068812847137, total_test_acc:0.9289974439079807\n",
      "------------------------------ \n",
      " epoch: 8\n",
      "total_train_loss:0.23114500966939058, total_test_acc:0.9142289122408407\n",
      "------------------------------ \n",
      " epoch: 9\n",
      "total_train_loss:0.25732120221311394, total_test_acc:0.9204771371769384\n",
      "------------------------------ \n",
      " epoch: 10\n",
      "total_train_loss:0.19249961958690123, total_test_acc:0.926725362113036\n",
      "------------------------------ \n",
      " epoch: 11\n",
      "total_train_loss:0.20548844987695866, total_test_acc:0.9372337404146549\n",
      "------------------------------ \n",
      " epoch: 12\n",
      "total_train_loss:0.29025672619993037, total_test_acc:0.9088327179778471\n",
      "------------------------------ \n",
      " epoch: 13\n",
      "total_train_loss:0.19752990589900452, total_test_acc:0.9508662311843227\n",
      "------------------------------ \n",
      " epoch: 14\n",
      "total_train_loss:0.13389014845544642, total_test_acc:0.9531383129792672\n",
      "------------------------------ \n",
      " epoch: 15\n",
      "total_train_loss:0.1313338034532287, total_test_acc:0.955978415222948\n",
      "------------------------------ \n",
      " epoch: 16\n",
      "total_train_loss:0.12676333392208272, total_test_acc:0.952570292530531\n",
      "------------------------------ \n",
      " epoch: 17\n",
      "total_train_loss:0.12082837356085127, total_test_acc:0.9673388241976711\n",
      "------------------------------ \n",
      " epoch: 18\n",
      "total_train_loss:0.10807360850951889, total_test_acc:0.9622266401590457\n",
      "------------------------------ \n",
      " epoch: 19\n",
      "total_train_loss:0.11104189577427777, total_test_acc:0.9571144561204203\n",
      "0.9673388241976711\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-11T09:36:04.341070Z",
     "start_time": "2024-12-11T09:36:04.133436Z"
    }
   },
   "cell_type": "code",
   "source": [
    "epochs = np.arange(1, epochs+1)\n",
    "\n",
    "# 绘制loss曲线图\n",
    "plt.figure()\n",
    "plt.plot(epochs, losses_train, label='Loss_train')\n",
    "plt.plot(epochs, losses_val, label='Loss_test')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training Loss')\n",
    "plt.legend()\n",
    "\n",
    "# 绘制accuracy曲线图\n",
    "plt.figure()\n",
    "plt.plot(epochs, accuracy_train, label='Accuracy_train')\n",
    "plt.plot(epochs, accuracy_val, label='Accuracy_test')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Training Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ],
   "id": "9a07a877f97701c4",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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06dM5evRovtNk8+fPp3///ri5ueV57a233qJv377UrFmT2NhYPv/8cxITExk5cmTZvmEhhBBCVBpmDZIGDx5MfHw8U6ZMITo6msDAQNasWYOfnx8A0dHRREVFGdrrdDq++eYbTp06hZWVFV26dGHXrl34+/sbXff06dPs3LmTDRs25HvfK1euMHToUOLi4vDw8KBdu3bs2bPHcF8hhBBCCI2iKIq5O1EZJSYm4uLiQkJCguQnCSGEEJVEcT6/zb66TQghhBCiIpIgSQghhBAiHxIkCSGEEELkQ4IkIYQQQoh8SJAkhBBCPIh0WZCZYu5eVGpmLQEghBBCiDKQehMWPwmxJ6BON2g6GOr3Ais7c/esUpEgSQghhHiQpCfAz09BzBH1+9Pr1IeNMzR6Ug2Y/DqChUwm3Y8ESUIIIcSDIiMZljwN0ZFg7w79Z8PlPXB4OSRchoO/qA/n6tDkaWg2BKo1NHevKywpJllCUkxSCCFEhZKVBr8+Axe2g60LjFoNXk3U1/R6iNoNh0Ph2F+QkXDnPK8m0HQINBkETl7m6Xs5Ks7ntwRJJSRBkhBCiAojOxOWPQdnNoC1I4z4C2q0zr9tVro6/XZ4mdpen60e11hArUfVgKnBE2DjWG7dL08SJJUDCZKEEEJUCLps+H00nFgFlnYwbAX4dyjauSnxcGylOh13JfzOcSsHaNgHmj4DAY+C9sHJzpEgqRxIkCSEEMLs9Hr4c7w6KqS1hqGhUOexkl0r/hwc+Q0OhcKtC3eOO3pC4CBoNhi8moJGY5q+m4kESeVAgiQhhBBmpSjwz0SIWAgWlvDMz9Cgt2mue2W/mr90dCWk3bzzmkcDdXVck6ehim/p72UGEiSVAwmShBBCmI2iwPr3YM8sNZdo4DwIHGj6+2RnwtmN6kjVqbWgy8h5QQP+HdXpuEb91ETxSkKCpHIgQZIQQgiz2fQZ7Phafd5vFrR4ruzvmXYbjv+l5i9d2nnnuKWtWqiy6WC1cKXWqsBLKIpCepaetCyd+sjMJi3znu+zdIZjtdwd6NKgmknfhgRJ5UCCJCGEEGax4xvYNEV93vtraPt8uXfh5tWzRG1bhO/lv3FLu5O/lGThwh77zmyx7soR6pCapbsTFGWqgVBx9Gvuw3dDWpi078X5/H5w0tWFEEKIB92e2XcCpMenlGuAlJKRTdjx6/xx8Co7z8ah07cDgmisucgA7U6e1O7CQ5/A48mreJxVnNd78aeuI3/qO3BT8cxzPWtLC+yttdhZqQ9bKy121lrsrXOeW2lp5edabu8vPzKSVEIykiSEEKJcRSyEv19Xn3d+F7pMLvNbZun07DwTx5+RV9lw7LrRSFCzGi60qOmKXU6gY69VCEjaR92YNfjEbMJSl2Zom+bVmrQGg1Aa98fW2QNbKy1aC/OskpPptnIgQZIQQohyc2gZ/PEioEDwBHUUqYyW4iuKwsHLt/nz4FVWH44mPiXT8Jqfmz39m1enX3MfankUUmwyIwlOrlbLCVzYBopePW5hBfV6qAnf9XqCpU2ZvIfCSJBUDiRIEkIIUS6Or4LfRoGigzbPQ+9pZRIgnbuRzF8Hr/LXoWtcik81HHd3tKZPUx/6NfehuW8VNMW9d2I0HP1dDfSuH7lz3NYFGvVX94/zbVduG+5KkFQOJEgSQghR5k5vgNBnQZ8FzZ+DJ2eaNJiITUxn1aFr/BV5jSNX7+znZm+tpUdjL/o196FjHXcstSa65/VjajmBw79B0rU7x6vUhCbPqCvkPOqZ5l4FkCCpHEiQJIQQokxd2A5LnobsdGg8QK2FZKEt9WWT0rNYdzSGvyKvsetcHPqcKEBroaFzPQ/6Nffh8Uae2FuX4douvQ4u7lTLCRz/CzKT7rzm00LdPy5wIDh6mPzWEiSVAwmShBBClJmovfDzU5CVAvV7wzOLC60/dD+Z2Xq2nb7Bn5FX2Xj8OhnZesNrLWtWoX+L6jzRxBs3x/LPESIzFU6vVafjzm5UpxUBNFp1ZOmp2Sa9nZQAEEIIIUxBl6Vu+VEGOUA6vcKl+BT0inp5DaDRaLC9cRjPP57GIiuFdN9HiH98FpqkbDSabCw0GjQAGtCgwUKjnqOem/P8rmsdv5bIn5FXWXMkmtupWYZ71/ZwyEnArk5NN3uTv7disbZXR40CB0LyDXXD3UOhcO2A2St5y0hSCclIkhBCPGAykiD6MERHwrVIuHYQ4s+Ckzf4BYNfe/DrAO71S5wXlJ6l49+zcaw/FsPGE7HcvGvlGEA9zWWWWX+GqyaZvfoGjMz8D+mYZnSnmpMNfZv58FSL6jT2cS5+AnZ5izujrn6rUtOkl5WRJCGEEKIwGckQc1gNhqIj1YAo7gyQz7hB0jV1ddbR39Xv7apCzfY5gVMweDUFbcEfp4npWWw5GcuGY9fZeiqWlMw7tYbsrLTYWFmgKOCrXGWh8gWummQOK3V4RfkPWNlhrajdUlDQK+oSfQV1+7b7cbSxpGegF/2bV6d9bTez1SYqEfe65u6BjCSVlIwkCSFEJZGZkneEKO40+QZEztXVxGHv5urXag3h5nm4tAsu/QuXwyE7zfgca0fwbZsTNHUAn5bEpkPY8eusP3ad3efiyNLduZe3iy3dG3nSo7EXbQKqYqW1gFuXYEEvSLwKnk1g5Cqwr3rft6YoCoqivhO94XnOV0Wtal2pAqNyICNJQgghHk6ZqRBzRA2EDCNEp+8UM7ybk48aCPk0zwmKmoNjPpupulSHgE7q8+xMiD4EUbvUwClqN6QnwLnN6gPIwpKL+jrc1DcAfX2s9fWo6eFOj8Ze9GjsRdMaLsZTXYnXYPGTaoDkXg+G/1GkAAnu5CABaJFgyNRkJKmEZCRJCCHMLPUm3Dh1Z9rs2kGIO1VAQOR91whRc/WrU979xIpDURSOXb3NgX3/knR6O37JkQRZnMRDk2DcTmOBxrsZ1MyZnqvZHhzc1BeTb8DC3mog5+oPo9eCs0+p+iUKJyUAyoEESUIIUQ4URR1puXFSDSRunLrzNTUu/3McvdRA6O6gyMnLJN3R6RX2X7zJ+mPX2XA8hiu37ky9aS00tAtwZVBAJl3szlIldp862nT7Ut4LeTRQA6bL4XD9KDjXgNFrwNXPJP0UBZPpNiGEEJWLLhtuXVRHgu4OhOLOGBcavEe2U3WSnOuR5tGUTI+mZHs1w8LZG2tLC6y1FlhbWmCltcAqW4+VVlOiFV3pWTp2nYtj/dHrbDxx3WgvM1srCx6p60GPxl481rAaVeytc17pDIxVnyZcgUu71ZymqN1qwJf7AHCoBiP+kgCpAjL7SNKsWbOYNm0a0dHRNG7cmBkzZtCpU6cC2//444/MnDmTixcvUrNmTd5//31GjBhheH3hwoWMHj06z3lpaWnY2tqW+L73kpEkIYQogaw0dVm9USB0Wj2my8z/HI0WqtYCj/rqw70+eNTjlM6LfnMiSc/KZ3qtAHcCJw1WOc+NgimtxvDcxtICnV4h/MJNoxVpLnZWPNawGj0ae/FIXQ/srItZBTslTg2WLu1WR5m6fqAmiItyUWlGkpYtW8bEiROZNWsWHTp04KeffqJXr14cP36cmjXz1kWYPXs2kydPZu7cubRp04bw8HCef/55XF1d6du3r6Gds7Mzp06dMjr37gCpuPcVQghRQtePqYUBb5xSR4luXSLfVWUAlnbqsu+7AiHc66sBkqW1UdP0LB2vzdxJepYed0cbHGy0ZGbrydLpycj5mpmtN2y5kStTpydTV/SgKpeXsy3dG6sr0trmrkgrKQd3aNhXfYgKzawjSUFBQbRs2ZLZs++UHG/YsCH9+/dn6tSpedoHBwfToUMHpk2bZjg2ceJE9u/fz86dOwF1JGnixIncvn3bZPfNj4wkiQrlxmn4+3XITFaH7Yu4MkaIMqUo8ENLdQn93Wyr5ARC9XJGhxqoz118i1yk8YM/j/DLnijcHW1YN7ET7gVsp6HTK3kCpyzd3cGUYjiWma0GUMbtFAKru9C0ugsWspT+gVApRpIyMzOJiIjg3XffNTrevXt3du3ale85GRkZRiNCAHZ2doSHh5OVlYWVlbqvTXJyMn5+fuh0Opo3b85nn31GixYtSnzf3HtnZGQYvk9MTCz6mxWirCgK7JsHGz68U7tl3bswYI55+yUEqKvNbp4HK3vo/lnO6FB9cPAo1TYf64/F8MueKACmP9OswAAJ1GRqrYUWW6vSbwwrHj6lGC8snbi4OHQ6HZ6exkswPT09iYmJyfecHj16MG/ePCIiIlAUhf379xMSEkJWVhZxceoqhwYNGrBw4UJWrVrF0qVLsbW1pUOHDpw5c6bE9wWYOnUqLi4uhoevr29p3r4QpZd0Xd0hfM1baoDk2w40FnB4GZxeb+7eCQHH/lC/1usBbcaptYYcq5UqQIpOSOM/Kw4D8MIjtXiknul3iRcil9mCpFz3rjRQFKXA1QcffvghvXr1ol27dlhZWdGvXz9GjRoFgFar/pXQrl07hg0bRrNmzejUqRPLly+nXr16/PDDDyW+L8DkyZNJSEgwPC5fvlzctyqE6ZxcDbPbw9kw0NpAzy/V+irtXlZf/3uiWuBOCHNRFDj2p/q88VMmuaROr/DGskhup2bRpLoLb3Wvb5LrClEQswVJ7u7uaLXaPKM3sbGxeUZ5ctnZ2RESEkJqaioXL14kKioKf39/nJyccHd3z/ccCwsL2rRpYxhJKsl9AWxsbHB2djZ6CFHuMpJh1WsQ+iykxqvbF7y4DdqNV3M5uryvJrkmXVOn4IQwl6sHICEKrBygzuMmueT/bTvHnvM3sbfW8v3QFlhbmv3vfPGAM9tvmLW1Na1atSIsLMzoeFhYGMHBwYWea2VlRY0aNdBqtYSGhtKnTx8sCkj2UxSFyMhIvL29S31fIczqyn74qRMcWAxooMPr8Pwm46XD1vbw5Ez1+YFFcH6rOXoqBBxbqX6t30v9vSylA1G3mB52GoBPn2xMgLtDqa8pxP2YtQTApEmTGD58OK1bt6Z9+/bMmTOHqKgoxo8fD6hTXFevXmXx4sUAnD59mvDwcIKCgrh16xbTp0/n6NGjLFq0yHDNTz/9lHbt2lG3bl0SExP5/vvviYyM5McffyzyfYWoUHTZsONr2PYVKDq1Mu9T/3dnL6l7+XdQ8z/2zYNVE+ClXWDjWL59Fg83E0+1JaZn8XroQXR6hb7NfBjUqkaprylEUZg1SBo8eDDx8fFMmTKF6OhoAgMDWbNmDX5+atXR6OhooqKiDO11Oh3ffPMNp06dwsrKii5durBr1y78/f0NbW7fvs0LL7xATEwMLi4utGjRgu3bt9O2bdsi31eICiP+HKx8Aa7uV78PHARPfAN2VQo/r9snavL27Uuw+TPo9WVZ91SIO67sh8QrYO0IdbqV6lKKovDhn0e5fDONGq52/PepwBJVzRaiJMxecbuykjpJokwpChz8Gda+C1kpYOOiBkdNny76Nc5ugl8GABoYsw5qtiuz7gphZN17sOdHaPIMDJxbqkutiLjCm78dQmuhYfmL7Wnl52qiToqHVXE+vyXrTYiKJiUelg1TE7SzUsCvI7z0b/ECJIA6j0GLYYACf72ibgchRFnT6+8s/S/lVNvFuBQ++usoABMfqysBkih3EiQJUZGc2agu7T/5D1hYweNTYOQqqFLCulzd/6vuiB5/Frb+z7R9FSI/V8LV1ZU2zlC7a4kvk5mtZ0LoQVIydQQFVOXlLnVM2EkhikaCJCEqgqw0WPM2LBkIydfVbRqe36yuYLMoRaVguyrQ51v1+a7v1WXZQpSl3FGk+r3ByrbwtoX4JuwUh68k4GJnxbeDm6OVLUGEGUiQJIS5XYuEnzpDeM5WIkHj4YWt4N3UNNdv0FtN+Fb08NerkF3ATutClJZef2dVW+CAEl9m55k4ftqm7vf25cCm+FSxM0HnhCg+CZKEMBe9DnZ+C/O6qbujO3rBsBXqSjQrE38o9PoK7N0h9hjsnG7aa4syp9crVIo1Npf3QHKMutCgVpcSXSI+OYM3lkcC8GxQTXoGepmwg0IUjwRJQpjD7ShY1Bc2fgL6LGjYF17eXerl0gVycIPeX6nPt0+DmKNlcx9hUjq9wtzt52n66Qbe++OIubtzf7lTbQ37gKV1sU9XFIW3fz/MjaQM6lZz5MMnGpm4g0IUjwRJQpQnRYHDy2F2B7j0r1pHpt+P8MzPYF+1bO/deAA06AP6bHW1my67bO8nSiUqPpWhc/bw3zUnSM7IZmn4ZSIv3zZ3twqm18Hxv9TnJVzVtmjXRTafjMXa0oLvh7bAzroU+XhCmIAESUKUl7TbsGIsrHweMhKhRlsYv0Ndpl8exfE0GrXWkq0LREfC7pnFOj0tU4dOXwmmfCo5RVFYsvcSPb/bTvjFmzhYa2lWwwWAbzacMnPvChG1W110YFsFAjoX+/QT0Yl8sfYkAO/1akBDb6k/J8xPgiQhyoOiwO9j4OgK0GjVjWhHr1U3oy1PTl7QM6cUwJYvIO5Moc0VRSHi0k1eDz1I00/X8+TMnVy5lVoOHX04xSSkM3LBPt7/4yipOUvf1018hJnPtsRKq2HHmTh2n4s3dzfzdzRnr7YSTLWlZep4belBMrP1PNagGiOD/U3fPyFKQIIkIcrDyX/g3CbQWqvVrzu/A1oz7QrUbKia+6TLUFe76fV5mqRmZrM0PIonvt/JwNm7+SvyGlk6hWPXEuk381/2Xbxpho4/uBRF4Y+DV+j+7Ta2n76BjaUFH/ZpxNLn2+Fb1R7fqvYMbqPWyvp6w6mKl8Sty4YTq9TnJZhq+2z1cc7GJlPNyYavBjWVbUdEhSFBkhBlLTNV3aYBIHgC+LYtvH1Z02igzww1H+ryHth3Z9uI8zeS+fTvYwR9sYnJK49wPDoRG0sLnm5Vg4Wj29DYx5n4lEyenbuH0PCogu8hiiw+OYOXfjnAG8sOkZieTbMaLqye0ImxHQOwuKs20Gtd62JjaUHEpVtsPXXDjD3Ox6V/IeUG2FUt9lTbuqMx/Lo3Co0Gpj/THDdHmzLqpBDFZ9YNboV4KPw7AxKiwLkGdJpk7t6oqviq1bxXT0LZ+Ak7NC2Zc1jPzrNxhiZ+bvYMC/Lj6dY1qGKvTp+0DajK278dZvWRaN5deYSTMUl88ERDLLXy91ZJrD8Ww3srjxCfkomlhYbXH6vLS4/Wzvfn6elsy8hgf+ZsP8/XG07RuZ6HURBlVoZVbX1Ba1Xk06IT0nh35WEAXnikFh3rupdF74QoMfk/mxBl6eYF2DlDfd7jv2DtYNbu3O1G/We54tIKTVYq2r9fZ+fZG2g00K1hNRaObsOWNx/l+UdqGQIkAHtrS2Y+24JJj9cDYOGui4xasI/bqVKgsjgS0rKYtDySF3+OID4lk/qeTvz1agdee6xuoQHn+M61cbSx5Ni1RNYejSnHHheihFNtOr3CxNBIbqdm0bSGC28+Xr+MOihEyUmQJERZWv++mvsT0Bka9TN3b1AUhX0XbzJh6UGCv9zCs7HDSFOs6aA9xv81PMr2t7swb2QbHq1frcBRCo1Gw4TH6vJ/w1piZ6Vl59k4+v/4L2djk8r53VROO87coOeM7aw8cBULDbz0aG1WvdaBxj4u+Z+Qdht2TIerB6jqYM2YjgEATA87VTFWG17cAanxYO8G/p2KfNrsrWfZe0Fdvff9kBZYW8rHkah45LdSiLJyJgxOrQYLS7XitRmTUVMyslmy9xK9vtvB0/+3m1WH1ERsN9/6nGkyEYCe137EV3uryNfsGejNipeCqV7FjovxqTz14y62nIwto3dQ+aVkZPPBn0cYPj+c6IR0/N3s+W18e/7TswE2lgXUA7oSAT91gk2fwq+DITOVcZ0CcLGz4tyNFP44eLV830R+DFNtTxZ5MULEpVt8u1FdWTmlXyD+7hVnhFWIu0mQJERZyM6Atf9RnweNh2oNzNKNs7HJfLLqGO2+2MT7fxzlZEwStlYWDG7tyz+vdeSPlzvQdMC7UKONWrvpn4lquYIiauTjzKpXO9DWvypJGdmMWbSPn7adq3irr8xs38Wb9P5+B7/sUZPdR7b3Y83rnWjlV0ABUUWBXTMhpLtanR0gJRYiFuBsa8VLj9YGYMbG02Rm512dWG50WXDib/V5EafaEtOzeD30IDq9Qr/mPgxoWb0MOyhE6UjithBlYc8suHkOHKpB5/+U662zdXo2nrjO4t2X2HVXTR1/N3uGtfPj6Va+uNjflVxroVWrfv9fRzizQa0I3mxwke/n5mjDL+OC+HjVUZaGX2bq2pOcikniiwFNsLV6uCsmp2fp+DbsNHN2nEdRwMfFlmlPN6NDnUISlFNvwp8vwel16veN+qlB7IYP4N/voPUYRrb3Z/7OC1y5lcayfVEMb+9fLu8njwvbIO0mOHiAX4f7NlcUhQ/+OMqVW2n4VrXj8/6BstxfVGgSJAlhaglXYds09Xn3z8C2fCoHxyalExp+mV/3RhGTmA6AhQa6NvBkRHs/OtZxL3g1lEd9NZjb/Bms+w/U7gKO1Yp8b2tLC754qgkNvJyZ8s9xVh68yvm4FOYMb0U1Z1tTvL1K5+jVBCYtj+T09WQAnm5Vgw/7NsLZtpDVX5d2q1XZE6+C1gZ6fgGtx6ojNuFz1FGl/Quwa/8yr3apw8erjvHD5rMMauVrni08ijnVtuLAVVYduobWQsN3Q1rgVNjPQogKQKbbhDC1sA8hKwV8g6Bp0UdkSkJRFHadi+OVJQcInrqZ6WGniUlMx83Bmpcfrc32d7owb2RrHinKcvEOr4NXU0i7BWveKnZfNBoNI4P9WTymLS52VkRevk3fmTs5fOV2yd5cJZWl0/PdxjP0//FfTl9Pxt3RhrkjWjPt6WYFB0h6Pez4BhY+oQZIVWvDuI3QZpyay2ZpDZ3eVNv+OwOy0hjS1pfqVeyITcpg8e6L5fX27sjOhBP/qM+LMNV2IS6Fj/5SN1ae9Hg9WtZ0LcveCWESEiQJYUoXdqhbj6CB3tPKLFk7IS2LBf9e4PFvt/Ps3L2sPhJNtl6hZc0qzBjcnF2Tu/JOzwbUcLUv+kW1Vuq0m4WlulHpsT9L1LcOddz565UO1KnmyPXEDJ7+v938FVkBEozLwZnrSQycvYtvN54mW6/Qu4kXG954hMcbeRZ8UnIsLBkIm6aAooMmz8CL28C7qXG7Zs+CS011f7SIhdhYanm9W10AZm87R1J6Vhm+s3xc2Abpt8HRE/yCC22ama1nwtKDpGbqaFerKuM71y6fPgpRSjLdJoSp6LJh7Tvq89ZjwLuZyW9x9GoCP+++xKpD10jL0gFgb62lf4vqDAvyo5FPKaf2vJtCx0mw/St1NCngEbAvILm4EP7uDvzxcjCvh0ay+WQsr4dGciomibe61y/3AogpGdn8ezaOUzFJWGotsNJqsLG0wEprgXXOVyutxT3HNPkcs8D6rtfvrmek0yuE7LzAtA2nyMzW42JnxZR+jXmymU/hOTfnt6kbHidfB0s7eOJraP5c/sG1pbVajPSfiWrtrVajGNCiOv+37Rznb6Qwf+cFJnarZ/KfX4Fyp9oa9VPz2grxzYZTHLmaQBV7K2YMboG2ohTBFOI+NIosQymRxMREXFxcSEhIwNlZdqsWwJ7/U/N57KrCaxElCi7yk56l4+9D1/hlbxSHLt82HK/n6cjwdn70b1HdtLkd2Rnw0yNw4yQ0HQIDfirxpXR6hWnrT/F/284BaqHKGUNa4GhTtn+fXYhLYfPJWLaeimXv+Ztk6ky/AsxCgyGAAkhKzwbg0foefDmwKZ6F5WLpdbDtK9j2JaCARwN4eiFUa1j4TbMz4YeWkHAZen4J7cbzz+FrvPrrQRxtLNnxThdcHYq3uWyJZGfCtDqQkaBu1FzISNKOMzcYPj8cgDnDW9G9sVfZ90+IQhTn81tGkoQwheRY2PJf9fljH5okQLoQl8KSPZf4LeIKCWnqVIqVVkOvQG+GtfOjjb9r2awMsrRRp93mPw6HQyFwANTrUaJLaS00vNurAQ28nHhnxWE2nohlwKx/mTeiDTXdijEVeB8Z2Tr2nr/JllOxbDkZy8X4VKPXa1a1p41/VTQaNWcoM1uvftUpZGbryNIpd45l68m8u022Xn39nkBLr0BGtp6MnCX4DtZaPuzTiMFtfAv/d0mMVkePLu5Qv28xXK2jZV2En4dhNOkN2PkttBpF70BvGnqf40R0Iv+37RyTe98n0DKF81vUAMnRC3zbFdjsyq1UXg+NBGBYu5oSIIlKR4IkIUxh46dqnSHvZtByZIkvk7t8/5c9UUb7qFWvYsezQTV5prUvHk7lsAFojdbQ/hXY9QP8PRFe2QO2BVSELoL+Larj7+7AC4v3c/p6Mk/+uJNZz7UkuHbJ9+q6djstJyi6wb9n4wzTj6AGk0EBbjxa34MuDapRy92h1AGloihk6RSjICvDEGzp8aliV/jKNYCzG2Hli5AaB1YO0HcGNH2meB1pPgy2fwOJV+DAIiyCXuSt7vUYu2g/i3ZfZEzHgMJHsUwhd6qtcX+wyD+1NSUjm3GL9nMzJZPGPs588ESjsu2TEGVApttKSKbbhMGV/TDvMfX52DDwbVvsS8QkpBO6L4ql4VFcT8wA1LSULvWrMaxdTTrXq1b+eRxZaTC7g1rvqdUo6PtdqS8Zk5DOiz/v59CVBCwtNHz8ZGOGt/Mr0rnZOj0Hom4bRotOxhhvg+LpbEOX+tXo0qAaHeq4l/mUXrHostSRxp3fqt97NlGn19zrlOx6++bD6kng5A0TIlEsbRg4excHom4zvJ0fn/UPNFnX88hKh6/rqn8UjFkPNfOOJOn1Cq/8eoC1R2Nwd7Rh1asd8KliV3Z9EqIYivP5LUFSCUmQJAA1t2RuV4iOVBNu+88q+ql6hV3n4vllzyXCTlw37MPl5mDNM218ebZtTXyrmm5KqkQu/gsLe6vPR/wFtR4t9SXTs3T8Z8Vh/oq8BsBzQTX55MnGWOWzsWt8cgZbT91gy6lYtp++QWJO3g+oOUEta7rSpUE1Hq3vQSNv54pZmPD2ZbX20eW96vdtxkH3/4JVKUZ7sjPg+5bqaFKvaRD0ArvOxfHs3L1YaTVsfvPRsvvdObkGQoeCkw+8cSzfkaQZG08zY+MZrLUWLH2hHa38ZLm/qDgkJ0mI8nLwZzVAsnGGbp8U6ZSE1Cx+i7jMkr1RXIhLMRxv4+/KsHZ+9Az0Kngvr/Lm3wHaPA/75sKqCfDSLrBxLNUlba20zBjcnAZezny1/iRL9kZxNjaZ2cNaUcXOiqPXEthy8gabT8Vy+Mpto11SXO2t6FxPnUJ7pK5H+SQpl8bJNWr17PTb6u/Ikz+oU1SlZWkDnd6A1W+qo1MtRxBc250Oddz492w83206w9dPm351JXDXVNtT+QZIa49EMyNnX7bPnwqUAElUajKSVEIykiRIvQk/tFK3ZegxFdq/XGjzyzdT+W7TGf4+dM2Q7OtoY8lTLarzXLuaNPCqoL9HGUkwKxgSotTSBk9MN1n9p00nrvN6aCTJGdl4Otug00NccoZRm8DqznSpX41H61ejuW+VyrF8PDsTNn4Ce35Uv/dpCYNCoGqACe+RAd+3UItP9v4a2j7PwahbPDVrFxYa2PBGZ+pUK11Am0dWmrqqLTMZxm4E3zZGLx+7lsCg2btJy9IxtmMAH/aRPCRR8chIkhDlYcsXaoDk0RDaPl9o0+uJ6QyZs4ert9MAaODlxPD2fvRrXr1i5c7kx8YJnvwOfn4K9oeoeTCd3zHJpR9r6MnKl4MZt2g/UTfVFWmONpZ0rONO1wbV6Fzfo+yTkE3t5gX4fTRcO6h+3+4VdZTR0sSjXpY20PENtZ7VjunQcgQtarrSraEnG09c59uw0/z4XEvT3vPsJjVAcvFVk/vvEpecwQuLI0jL0tGprjuTe5lnU2chTKmC/99ZiAoq+jDsn68+7/2VWq26AMkZ2YxesI+rt9MIcHfg66eb0rJmGS3fLyu1u0KPL2D9e2oCspUdBL9mkkvX83Ri1asdWHngKg28nGjtX9VQe6jSOfYnrHpNTWq2rQL9Z0OD3mV3v5Yj1AAp6RocWAxtn+fN7vXYdPI6q49E89LVBAKrl3xVYh53F5C86/c3M1vPS79EGH7HZw5taVRsU4jKSn6LhSguRYE1b4OiV/MyAh4psGmWTv3wOB6diLujNYtGt6WVX9XKFSDlav8KdP1Afb7hA9g3z2SXrmJvzZiOAQTXca+cAVJWOvwzCX4bqQZIvkEwfmfZBkiQk5s0SX2+81vIzqChtzN9m/oAMD3stOnulZUGp9aqzxsPMBxWFIWP/jrKvou3cLKxZO6I1rjYy8a14sFg9v8bzZo1i4CAAGxtbWnVqhU7duwotP2PP/5Iw4YNsbOzo379+ixevNjo9blz59KpUydcXV1xdXWlW7duhIeHG7X55JNP0Gg0Rg8vLylyJoro8HK4vAes7KH75wU2UxSFySuPsONMHHZWWuaPNG0BRbN45G112xJQk4YjfzVvf8wtKw32L4D/63BnZLHjGzBqNVTxLZ8+tBiuToEmXlUXEgBvPF4PrYWGzSdjibh00zT3OROmbtzsUhOq35nGW7z7EqH7LqPRwPfPtjB9HpQQZmTWIGnZsmVMnDiR999/n4MHD9KpUyd69epFVFRUvu1nz57N5MmT+eSTTzh27Biffvopr7zyCn///behzdatWxk6dChbtmxh9+7d1KxZk+7du3P1qvEGm40bNyY6OtrwOHLkSJm+V/GASE+EsA/V54+8BS41Cmz67cYz/B5xBQsN/PhcC5r5VimfPpa1xz6CoPHq879egaMrzdsfc0iKgU2fwfRG6l5q8WfB3h2GrVDzjwqZfjU5K9s7gesOdTQpwN2BQS3V381p609hkvU5x3L+nRv3N0y1/Xs2jin/HAdgcq8GdKlfrfT3EaICMevqtqCgIFq2bMns2bMNxxo2bEj//v2ZOnVqnvbBwcF06NCBadOmGY5NnDiR/fv3s3PnznzvodPpcHV1ZebMmYwYMQJQR5L+/PNPIiMjS9x3Wd32kNrwgVqFumoteHmPOt2Rj9DwKN5dqQbeXzzVhGeDapZnL8ueosDfE9Q8GAtLGPwL1O9l7l6VvejDsGcWHPkd9OpWMVSpqQaNLYaVqip5qWSlw/fNISka+nwLrcdw9XYaXaZtJVOn55exQXSsW/Lq5mSmqKvaslLh+S1QvSWX4lN4cua/JKRlMaBFdb55plnlnEYWD53ifH6bbSQpMzOTiIgIunfvbnS8e/fu7Nq1K99zMjIysLU1XuliZ2dHeHg4WVlZ+Z6TmppKVlYWVasa76V15swZfHx8CAgIYMiQIZw/f77Q/mZkZJCYmGj0EA+ZG6dgT05A3/PLAgOkLadief/PowC81rXOgxcggTqS0GcGNHka9NmwfASc22LuXpUNvV6td7SwD/zUCQ4tVQMk33bwzGJ47aCar2WuAAlyRpPeUJ/vmA7ZmYatbACmrT9ZutGkMxvUAMnVH3xakJSexdhF+0lIy6K5bxW+GNBEAiTxQDJbkBQXF4dOp8PT09PouKenJzExMfme06NHD+bNm0dERASKorB//35CQkLIysoiLi4u33PeffddqlevTrdu3QzHgoKCWLx4MevXr2fu3LnExMQQHBxMfHx8gf2dOnUqLi4uhoevbznlG4iKQVFg7TtqQFCvF9Trnm+zI1cSeGXJAXR6hQEtqzPp8Xrl3NFyZKGF/v8HDfqALhNCn4VL+f+BUyllJEP4XJjZSq0wfXEHaLQQOBDGbYax69VVXtoKski45Uh1w9mEyxC5BIBXutTBzkrLoSsJhB2/XvJr31VAUqfA66GRnI1NxtPZhjnDW2FrVUGKnwphYmZP3L73rw9FUQr8i+TDDz+kV69etGvXDisrK/r168eoUaMA0Grz/kf61VdfsXTpUlauXGk0AtWrVy8GDhxIkyZN6NatG6tXrwZg0aJFBfZz8uTJJCQkGB6XL18u7lsVldmJv+H8VtDaQM8v8m1y+WYqoxfuIzVTrRPzvwFNH/y/rrWWapHEOt3UkYYlz8CVCHP3qnQSrkDYR/BtI7UG0c3z6ihRh9dh4mH1/dZoZe5e5mU0mvQNZGfi4WTD6A7+AHyz4TR6fQlGkzKS4fQG9Xnjp/h6wyk2n4zFxtKCOcNbU62y1bESohjMFiS5u7uj1WrzjBrFxsbmGV3KZWdnR0hICKmpqVy8eJGoqCj8/f1xcnLC3d14vv3rr7/miy++YMOGDTRt2rTQvjg4ONCkSRPOnDlTYBsbGxucnZ2NHuIhkZmq1gcC9YOyaq08TW6lZDJyQThxyeoS7FnPtaycS9lLwtJGzUny7wSZSfDLAIg5au5eFd+VCPh9DMxoCv9+B+kJ6r9176/hjePw+JRCE/UrhFYjwdFTHU06pK48fPGR2jjZWnLqehJ/H75W/GueWQ/ZaVC1Fn/FuDF76zkAvhrU9MFZjCBEAcz2f3Fra2tatWpFWFiY0fGwsDCCg4MLPdfKyooaNWqg1WoJDQ2lT58+WNy1h9C0adP47LPPWLduHa1bty7kSqqMjAxOnDiBt7d3yd6MeLDt/Fb90HHxvfOX+l3Ss3SMW7yf8zdS8HGxZcGoNjjZPmR1YqzsYGgo1Gir7lO2uB/cMGGNnrKiy1YLQM7vDvO6wtEVoOjUgG9oKLwaoVZTL+V+deXGyu7O7+h2dTTJxd6KFzqpgf23YafJ0umLd82cqbbrvr14Z4W6GOGlR2vTr3l1k3VbiIrKrH/qTpo0iXnz5hESEsKJEyd44403iIqKYvx4dXnx5MmTDSvSAE6fPs0vv/zCmTNnCA8PZ8iQIRw9epQvvrgz/fHVV1/xwQcfEBISgr+/PzExMcTExJCcnGxo89Zbb7Ft2zYuXLjA3r17GTRoEImJiYwcObL83ryoHG5eUEcVAHr8F6yN6xzp9AoTQyOJuHQLJ1tLFo5pi5fLQzr9YOMIz/0G3s0gNQ4WP6n+/Cqi9ETY/SP80EItAHl5L1hYQbNn4cUdMOofdbVePhu4VnitRuWMJkWpSebA6I4BuDlYczE+lRURV4p+rYwktT4S8OaxWmRk63msQTXe6l6/DDouRMVj1v8DDB48mBkzZjBlyhSaN2/O9u3bWbNmDX5+fgBER0cb1UzS6XR88803NGvWjMcff5z09HR27dqFv7+/oc2sWbPIzMxk0KBBeHt7Gx5ff/21oc2VK1cYOnQo9evXZ8CAAVhbW7Nnzx7DfYUwWP8e6DKg1qPQ8EmjlxRF4bN/jrPuWAzWWjU/o56nk3n6WVHYVYFhf6j72SVFq4FSQjE+lMvarYuwbrJa32j9e3A7Cuzd1CKZbxyFp2aDd+HT8xWelR10mKg+3/E16LJwtLHkpUdrA/DdpjOkZ+mKdq1T6yA7nava6uxM9qJONUdmDGleOTYZFsIEzFonqTKTOkkPgdMb4Nen1TpAL+0CD+O/nuftOM/nq08A8P3QFjzZzMccvayYkmJgQS816dmtDoxaA0755xqWOUWBqD2w50c4uVrdTgbAowG0ewmaDlYDiwdJVpqaW5USC0/+AC1HkJ6l49FpW4lJTOejPo0Y0zHgvpdRlg5Fc2oN32f3Z77Vc/z1Sgf83R3K4Q0IUXYqRZ0kISq07AxY9x/1edD4PAHS34euGQKk93o3kADpXk5eMGKVmscVfxZ+7g+pJtoeo6h0WXD4N5jbBRb0VFcoKnqo/ZhaGfvlPerU1IMWIEFObtJE9fn2aaDLwtZKy4TH6gIwa+tZUjKyC79GeiL60+pU21olmFnPtZQASTx0JEgSIj+7f1RHQRw9ofN/jF7aez6eN5cfAmBUsD/Pd8q72k2g7l02cpVauyf2OPz8lLpirKyl3VKT7Wc0hZXj4NpBsLRV6wi9vBeGr1RLFjzo5RlajQaHauqU4qFQAJ5uXQM/N3vikjNZuOtioacf37oMrZLFGX11Bvd6nA51SlGxW4hKSoIkIe6VcFX96xvg8c/A9s5w7JnrSTy/eD+ZOj09GnvyYZ9GD34tpNKoWksNlOzdIToSljyt1t0pC3Fn1U13pzeCjZ9A0jU1yO3yAbxxDJ78Hqo1KJt7V0TW9tBhgvo8ZzTJSmvBxG7qaNJP286RkJb/TgVnY5O5vltN+r7k1Z2RHe4/NSfEg0iCJCHuteEDtTBizfbQ9BnD4euJ6YxasI/E9Gxa1qzCd0NaSAJrUXjUhxF/qgUZL+9Vq1dnpZnm2ooC57fBr4PVytj75qn/dp5NoP9smHgEOr8NDg/pKEjrMeDgAbcvweFlADzZrDp1qzmSmJ7N3O15t2NKSM1i4qKtBKOOlnZ+6nn5Q0A8tCRIEuJuF7aru51rLKDXV4YpmaT0LEYt2MfV22nUcndg3sg2shVDcXg1gWErwdpR/RkvHwHZmSW/XnYGHFwC/9dJXUF3eh2gUbeMGfk3jN8BzZ8tcH+9h4a1AwQbjyZpLTS8mbOEP+TfC8QlZxiaZ+v0vLr0APVu7cBGk022WwOsvBubo+dCVAgSJAmRS5cFa3Pyj1qPMSwFz9LpeXnJAU5EJ+LuaM3C0W2p6mBtxo5WUjVaw7PLwdJO3TB1xVi1mGNxpMTB1i/h20D462W4fgSs7KHN8/BaBDwbCgGPPPj5RsXRZqw63XnrIhxeDkCPxp40reFCaqaOWVvOGZpOXXuSHWfieNIqHADLJgPM0WMhKgwJkoRpJUaribKV0aFQNcHYrip0eR9QayG9u+IIO87EYWelJWRUG2q62d/nQqJA/h1gyBLQWsOJVWqgoy9CBejYE/DXq2q+0dYv1KXtTj7Q7RM13+iJr8Gtdpl3v1KydrgnNykbjebOaNIvey9x7XYay/dfZv7OCziTzCMWamVtGvc3T5+FqCAkSBKmc34b/BgEc7tC9CFz96b4IhaqXzu8DvZVAfh24xlWHLiC1kLDrOda0rRGFbN174FR5zF4eiFotGqezOo31Nyie+n1cGajuipuVjs4+LNa2NOnJQycr2422/ENw7+VKESbcWrRzFsX4Ig6mvRIXXfaBlQlM1vPpOWRfPCHut/etMDLWChZUK1xntIXQjxsJEgSpnFwibqxaUaCWotm/wJz96h4rh+Dq/vVwpHNnwMgNDyK7zepmx5/3j+QLg2qmbOHD5YGT8DAuWruV8RCtfp1bqCUmQr7Q9TAaMlAOLdZbdfwSRizHp7fDE0GgfYh2x+vNPLkJqmjSW/3UIOgPedvkqnT07OxF92V3Wq7wKfM1FkhKg5Lc3dAVHKKAlv+e2fJfI22cCUcjvwG3T+vPBuDHvhZ/Vq/Fzh6sOVkLO//qf5lPaFrHYa2rWnGzj2gAgdCVro65bZnlhoIWdqqAVJaTuFJaydoORyCXgRXf7N2t9JrMw52fa/W/zryGzQfShv/qnSu58G20zdo4OXEN3180fywVW3fSIIkIWQkSZRcdgasfOFOgNTpTRizTq2Nk5msrhIzsfQsHbGJ6UXfe6oosjPgsFpsj5YjOXzlNi8vOYBOrzCwZQ3eeLye6e4ljLV4Dnrn7Ku4e6a611jaTahSE3p8AZOOQ8+pEiCZgo0jBL+mPs8ZTQL4alBTJnary+IxbXG4sA702epqRPc6ZuysEBWDjCSJkkm9CcuGwaV/1dySvjOg5Qj1tZYjYePHELHozjET+OfwNSavOEJSznYKNpYWONtZ4ZLzcLa1NDx3sbPCOeehvpZz3F5t52hjeaf2y8l/1CrNztWJqtKOMT/tIS1LR6e67vxvYBOpEVPW2j6vBqphH0KNNtD+Faj/BGjlf08m1+Z5+Pd7uHkOjv4OzYbg6WzLxG45fwgc+0P92lhGkYQA2eC2xB7qDW5vnlcrJ8efBRtneGYR1O565/XkWJjeUP2L9KVd4Fm6OiuZ2Xqmrj3Bgn8vlq7fd9FaaAxB1beZn9Ii6yBrqg5navoALt9Mo5G3M8tebIeTreS9lJvsDKlrVB52TIdNn6obD7+8904wmhIPX9cFRQevHZDVguKBVZzPb/lTTRRPVE7F5NR4cK4Bz/0Gno2M2zhWg/q91SXeEYug91clvl1MQjqv/HqAiEu3ABjfuTZvdq9HWpaOhNQsEtKySEzPIjFNfZ6QlkViWvad5+l3H1e/ZukUdHqFW6lZOKRdpYWNWrLgi+hWXFHSqF7FjgWj20iAVN4kQCofbZ+HXT+of+QcXQHNBqvHT/6tBkjezSRAEiKHBEmi6I79AStfVJdhezeHZ5epu73np9VINUg6HAqPf1qindZ3nY3jtaUHiU/JxMnWkm+ebkb3xur9rLQWONta4VvMayqKQnqW3hBAOez6Eg7Bdff2jG3xKNk6hb7NfPB0ti12f4WoFGycIPhV2DQFtn+lrhS00MLRnBxCmWoTwkCCJHF/igL/zlA3DQV164dB89VlxQWp1RVcakJCFBz/C5oNKfLt9HqF2dvO8c2GU+gVaOjtzP8Na4mfWyH3KyKNRoOdtRY7ay1eTlZwQf1g8Hz0eUYHyiae4iHR9gXj0aRaXeDiDvU1CZKEMJDVbaJwuiz4+/U7AVLQeLVicmEBEoCFxZ2k7YhFRb5dQmoWL/y8n2nr1QBpUKsa/PFysEkCpDzObYbEq2DnCg36mP76QlRUNk7Q/lX1+bav4Pifan0zn5ayklCIu0iQJAqWngi/PgMHFgEa6Pkl9PpSHZovihbPqbVvonbBjdP3bX70agJ9Zu5g44lYrC0t+N+AJkwb1LTsNpI9sFj92nSI5MOIh0/bF8C2CsSfgU2fqcdkFEkIIxIkifwlXIGQnupoi5U9DPkV2o0v3jWcfaBuD/X5gcJHk5bvu8yA2bu4fDONGq52rHwpmCFta5bd8vvkG3Bqjfq85fCyuYcQFZmts5qbBGqlfJC92oS4hwRJIq9rkTD3MYg9Bo6eMGo1NOhdsmu1Gql+PbRUXeJ9j/QsHe/8foh3VhwmM1tP1wbVWP1aJwKru5S8/0VxOFQtUVC9ValLFAhRabV9UR1NAqjeWi3iKYQwkCBJGDu1Dhb0huQY8GgI4zZC9ZYlv16dx8HJWy0ZcHK10UtR8akMmLWL5fuvYKGBt3vUZ96I1rjYl/HSe0W5M9VmwmKXQlQ6ts7Q5T31edvnzdsXISogCZLEHeFz1RpIWSlQ61EYu770f1lqLaHFMPV5xELD4Y3Hr9Pnhx0cj06kqoM1i8cE8UqXOlhYlEN168t7Ie60Oo3YeEDZ30+IiizoRXg3qlgrUIV4WEgJAAF6HWz4EPb8qH7fYjj0+dZ0u6y3GA7bv4YL28i+cY7pEVnM2noOgJY1q/Djcy3xdil+HaUSy93MtvEA9S9pIR52tmU8vS1EJSVB0sMuMxVWPq/uXwbw2EfQcRKYMmHa1U/dtuTcJlYv+pJZcf0AGBXsz3u9G2JtWY4DmumJdzbelYRtIYQQhZDptodZ0nVY+IQaIGmtYeB86PSmaQOkHOdqDgSgfdJ6nK0Vvh/agk+ebFy+ARKoAVJWKrjXA9+g8r23EEKISkVGkh5WsSfVTWoTotRiikOWgl97k99GURQW/HuRaeud2G7lTDXNbdb3SsO7mY/J71Ukdydsl1V5ASGEEA8EGUl6GJ3fCvO7qwFS1VowblOZBEjJGdm8uvQgU/45TppeS2RVtYyA97nlJr9XkVw/BlcjwMJSLSAphBBCFEJGkh4257bAkkFqjSDfdmqRSAc3k9/mzPUkxv8SwbkbKVhaaPjgiYZ0q18fZobCmTC4fRmqFHd72lLKTdiu3xscPcr33kIIISodGUl62Bz8RQ2Q6vWCEX+VSYD0V+RV+v34L+dupODlbMuyF9szqkMAGvc64N8JUNR+lKfsDLWAJEhtJCGEEEUiQdLDJila/dpkEFjZmvzy34ad5vXQSFIzdXSo48Y/EzrSys/1ToNWo9SvB39RSw+Ul5P/QNotcK6urrQTQggh7kOCpIdNbpDk5G3yS1+7ncbMLWcBeKVLbRaPCcLd8Z6NYxv0URPFE6/A2U0m70OBchO2Wwwr+ga9QgghHmpmD5JmzZpFQEAAtra2tGrVih07dhTa/scff6Rhw4bY2dlRv359Fi9enKfNihUraNSoETY2NjRq1Ig//vij1Pd9ICgKJOYESc6mD5IW7b6ITq/QvpYbb/dogDa/6tlWttBsqPr8Ppvemsyti2qyOhpo/lz53FMIIUSlZ9YgadmyZUycOJH333+fgwcP0qlTJ3r16kVUVFS+7WfPns3kyZP55JNPOHbsGJ9++imvvPIKf//9t6HN7t27GTx4MMOHD+fQoUMMHz6cZ555hr1795b4vg+M9ATITlOfm3gkKSUjm6V71Z/f2I4BhTdumbPp7am1kBRj0n7k6+AS9WutR9XClkIIIUQRaBRFUcx186CgIFq2bMns2bMNxxo2bEj//v2ZOnVqnvbBwcF06NCBadOmGY5NnDiR/fv3s3PnTgAGDx5MYmIia9euNbTp2bMnrq6uLF26tET3zU9iYiIuLi4kJCTg7FxJtraIPQGz2qm7fr97yaSXXrz7Ih/9dYwAdwc2Tep8/z3Y5ndX91Dr+iE88pZJ+2JEr4MZTSDxKgwKgcCBZXcvIYQQFV5xPr/NNpKUmZlJREQE3bt3NzrevXt3du3ale85GRkZ2NoaJxvb2dkRHh5OVlYWoI4k3XvNHj16GK5Zkvvm3jsxMdHoUemUUT6SXq8WjAQY3cG/aJvU5iZwH1gMer1J+2Pk3GY1QLJzVfOhhBBCiCIyW5AUFxeHTqfD09PT6LinpycxMflPwfTo0YN58+YRERGBoijs37+fkJAQsrKyiIuLAyAmJqbQa5bkvgBTp07FxcXF8PD1LecaP6ZQRvlIm0/GciEuBWdbSwa2rFG0kxr1BxsXuH0JLmwzaX+M5OY9NRsKljaFtxVCCCHuYvbEbc09W0MoipLnWK4PP/yQXr160a5dO6ysrOjXrx+jRo0CQKu9s2KpKNcszn0BJk+eTEJCguFx+fLl+763CqeMRpLm77wAwNCgmjjYFLE+qbU9NH1afV5WCdzJsWreE0AL2cxWCCFE8ZgtSHJ3d0er1eYZvYmNjc0zypPLzs6OkJAQUlNTuXjxIlFRUfj7++Pk5IS7uzsAXl5ehV6zJPcFsLGxwdnZ2ehR6eQmSTt5meySx64lsPt8PFoLDSPb+xfv5NwE7hP/QEqcyfpkcChULZxZvTV4NjL99YUQQjzQzBYkWVtb06pVK8LCwoyOh4WFERwcXOi5VlZW1KhRA61WS2hoKH369MHCQn0r7du3z3PNDRs2GK5ZmvtWemUwkhSy8yIAvZt441PFrngnezcFnxagz4JDS03WJ0Atd2DYzFZGkYQQQhSfWfdumzRpEsOHD6d169a0b9+eOXPmEBUVxfjx4wF1iuvq1auGWkinT58mPDycoKAgbt26xfTp0zl69CiLFt2Zrnn99dd55JFH+PLLL+nXrx9//fUXGzduNKx+K8p9H1gmDpJik9L5+9A1oAjL/gvSciRcOwgRi6D9q1DIlGexXN4L8WfAykFWtAkhhCgRswZJgwcPJj4+nilTphAdHU1gYCBr1qzBz0+tZRMdHW1Uu0in0/HNN99w6tQprKys6NKlC7t27cLf39/QJjg4mNDQUD744AM+/PBDateuzbJlywgKCiryfR9YJk7c/mX3JTJ1elr5udLct0rJLtJkEKx/Xw1oLu0C/w4m6ZthFCnwKbBxMs01hRBCPFTMWiepMqt0dZL0OvjMAxQdTDoBzj6lulx6lo7g/23mZkoms55rSe8mpQi8Vr2mBjVNB8OAOaXql9q5RPimPmSlwpgNUDPo/ucIIYR4KFSKOkminKXcUAMkjQU4VCv15f48eJWbKZlUr2JH90YFJ7wXSctR6tfjf6mb0JbW0RVqgOReH3zblv56QgghHkoSJD0scvORHKqBtnSzrIqiEPKvuux/dAd/LLWl/DWq3hI8AyE7HQ4vL921wDhh21Q5TkIIIR46EiQ9LEyYj7TjTBynryfjYK3lmTYmKKqp0dwpBxCxSF2ZVlIxR+HaAbCwgqZDSt83IYQQDy0Jkh4WJlzZlls88pk2vjjbWpX6eoBaWNLSFmKPwZX9Jb/OwZ/Vrw16g6OHafomhBDioSRB0sPCRIUkz8Ymse30DTQaGB1cwmX/+bFzVbcqATiwsGTXyEpXC0gCtBhhil4JIYR4iEmQ9LBIUusZ4VS6VW3zc4pHPt7Qk5pu9qXs1D1yN709ulJdoVZcJ/+B9NvgXANqdzFlz4QQQjyEJEh6WJhgJOlmSiYrD1wBSlE8sjA126kr0rJS4ejvxT8/N2G7xXNgoS28rRBCCHEfEiQ9LEyQuP3r3ktkZOsJrO5M24CqJurYXTQaaJkzTRZRzE1vb12EC9sADTR/ztQ9E0II8RCSIOlhUcrE7cxsPYt3XwLUUSRNWS2tbzYUtNYQHQnXIot+3sFf1K+1u4DrA145XQghRLmQIOlhkJUOaTfV5yUMkv45fI3YpAyqOdnwRJPS5TUVysENGvRRnx8o4miSXgcHl6jPW8hmtkIIIUxDgqSHQXJOPpLWRl1FVkyKohiW/Y8M9sfasox/bVrl1Ew6/Btkpty//dlNamK6XVVo8ETZ9k0IIcRDQ4Kkh8Hd+UglmCbbe+Emx64lYmtlwbNta5q4c/nwfwRcAyAzCY79cf/2uSNOzYaApU3Z9k0IIcRDQ4Kkh0Ep85FyR5EGtKyBq4O1qXpVMAuLoidwJ8fC6XXqc5lqE0IIYUISJD0MSrH8/2JcChtPXAdgTIcyWPZfkObPgYUlXAmH2BMFtzu0FPTZUKMNeDYqv/4JIYR44EmQ9DAoRSHJhbsuoijwaH0P6lRzNHHHCuHkCfV6qs8LGk1SFDiQsw2JjCIJIYQwMQmSHgYlHElKSMti+f7LQBkVj7yf3Arch5aqK/TuFbUH4s+AlQMEDijXrgkhhHjwSZD0MDAkbhdvJGnZvihSM3XU93SiYx33MujYfdTuCi6+6lYjJ1blfT23wnbgU2DjVK5dE0II8eCTIOlhYEjcLvpIUrZOz6JdavHIMR39y654ZGEstHem0e6dcktPgON/qs9bjizXbgkhhHg4SJD0oFOUEq1uW3cshqu303BzsKZf8+pl1LkiaDEMNBZwaSfEnb1z/OgKdY83jwZq0rYQQghhYsUOkvz9/ZkyZQpRUVFl0R9hahmJajABxQqScpf9P9fOD1srM24W61Id6jyuPr+7AvfdCdvmGOUSQgjxwCt2kPTmm2/y119/UatWLR5//HFCQ0PJyMgoi74JU8jNR7J1AWv7Ip1yIOoWB6NuY621YHi7CrAPWm4F7shfITsTYo7AtQNgYaUWkBRCCCHKQLGDpNdee42IiAgiIiJo1KgREyZMwNvbm1dffZUDBw6URR9FaZRgqi13FOnJ5j54OFWACtZ1e4CjF6TGwanVd0aRGvQGBzMklAshhHgolDgnqVmzZnz33XdcvXqVjz/+mHnz5tGmTRuaNWtGSEgIiqKYsp+ipIq5/P/q7TTWHVXPKdfikYXRWkKL59Tn4XPh8DL1eW5VbiGEEKIMlDhIysrKYvny5Tz55JO8+eabtG7dmnnz5vHMM8/w/vvv89xzz5myn6KkillIctGui+j0CsG13Wjk41yGHSum3IDo0r9qSQAXX6jVxaxdEkII8WCzLO4JBw4cYMGCBSxduhStVsvw4cP59ttvadCggaFN9+7deeSRR0zaUVFCxRhJSsnIZmm4mpBvluKRhXH1V4Oi81vU75s/p5YIEEIIIcpIsYOkNm3a8PjjjzN79mz69++PlZVVnjaNGjViyBBJqK0QEnNGkopQSPK3/ZdJSs+mlrsDXepXK+OOlUCrkTlBkubO9JsQQghRRoodJJ0/fx4/v8JXPDk4OLBgwYISd0qYUBFHknR6hQW7LgIwuoM/FhYVcFl9gz7qViWuAVClprl7I4QQ4gFX7CApNjaWmJgYgoKCjI7v3bsXrVZL69atTdY5YQKG1W2FjyRtOnGdS/GpuNhZMbBVjXLoWAloraDvd+buhRBCiIdEsRO3X3nlFS5fvpzn+NWrV3nllVdM0ilhInp9kUeScpf9D21bE3vrYsfOQgghxAOn2EHS8ePHadmyZZ7jLVq04Pjx4ybplDCRlBug6AANOHoW2Ozo1QT2XriJpYWGkcEVoHikEEIIUQEUO0iysbHh+vXreY5HR0djaSkjEBVK7lSbYzW11lABQnJGkXo38cbbxa48eiaEEEJUeMUOkh5//HEmT55MQkKC4djt27d57733ePzxx4vdgVmzZhEQEICtrS2tWrVix44dhbZfsmQJzZo1w97eHm9vb0aPHk18fLzh9UcffRSNRpPn8cQTTxjafPLJJ3le9/IqWrHFSqUI1bZjE9P5+7C6Aq7CLfsXQgghzKjYQdI333zD5cuX8fPzo0uXLnTp0oWAgABiYmL45ptvinWtZcuWMXHiRN5//30OHjxIp06d6NWrV4Gb5+7cuZMRI0YwduxYjh07xm+//ca+ffsYN26coc3KlSuJjo42PI4ePYpWq+Xpp582ulbjxo2N2h05cqS4P4qKrwhB0uLdl8jSKbT2c6WZb5Xy6ZcQQghRCRR7fqx69eocPnyYJUuWcOjQIezs7Bg9ejRDhw7Nt2ZSYaZPn87YsWMNQc6MGTNYv349s2fPZurUqXna79mzB39/fyZMmABAQEAAL774Il999ZWhTdWqVY3OCQ0Nxd7ePk+QZGlp+WCOHt3tPknb6Vk6luy9BMgokhBCCHGvEiUROTg48MILL5TqxpmZmURERPDuu+8aHe/evTu7du3K95zg4GDef/991qxZQ69evYiNjeX33383mkq71/z58xkyZAgODg5Gx8+cOYOPjw82NjYEBQXxxRdfUKtWrQKvk5GRQUZGhuH7xMTEorxN87pPIcmVB65yKzWLGq52dG/8gAeMQgghRDGVONP6+PHjREVFkZmZaXT8ySefLNL5cXFx6HQ6PD2NV115enoSExOT7znBwcEsWbKEwYMHk56eTnZ2Nk8++SQ//PBDvu3Dw8M5evQo8+fPNzoeFBTE4sWLqVevHtevX+fzzz8nODiYY8eO4ebmlu+1pk6dyqefflqk91ZhFDKSpCgKIf+qCdujgv3RVsTikUIIIYQZlaji9lNPPcWRI0fQaDQoigKARqN+yOp0umJdL/e8XIqi5DmW6/jx40yYMIGPPvqIHj16EB0dzdtvv8348ePzBEKgjiIFBgbStm1bo+O9evUyPG/SpAnt27endu3aLFq0iEmTJuV778mTJxu9lpiYiK+vb5Hfp1kUUkhy+5k4zsYm42hjyeA2Ffx9CCGEEGZQ7MTt119/nYCAAK5fv469vT3Hjh1j+/bttG7dmq1btxb5Ou7u7mi12jyjRrGxsXlGl3JNnTqVDh068Pbbb9O0aVN69OjBrFmzCAkJITo62qhtamoqoaGhRkndBXFwcKBJkyacOXOmwDY2NjY4OzsbPSo8Q5CUdyQpt3jk061r4GRbvFwyIYQQ4mFQ7CBp9+7dTJkyBQ8PDywsLLCwsKBjx45MnTrVkFBdFNbW1rRq1YqwsDCj42FhYQQHB+d7TmpqKhYWxl3WatWd4HNHtHItX76cjIwMhg0bdt++ZGRkcOLECby9C14FVulkZ0BqTmmEe3KSzlxPYvvpG2g0MDpYEraFEEKI/BQ7SNLpdDg6OgLqaNC1a2pysJ+fH6dOnSrWtSZNmsS8efMICQnhxIkTvPHGG0RFRTF+/HhAneIaMWKEoX3fvn1ZuXIls2fP5vz58/z7779MmDCBtm3b4uNjHAjMnz+f/v3755tj9NZbb7Ft2zYuXLjA3r17GTRoEImJiYwcObJY/a/QcvORtDZg52r0Um4uUvdGntR0sy/vngkhhBCVQrFzkgIDAzl8+DC1atUiKCiIr776Cmtra+bMmVPo6rD8DB48mPj4eKZMmUJ0dDSBgYGsWbMGPz91a4zo6GijmkmjRo0iKSmJmTNn8uabb1KlShW6du3Kl19+aXTd06dPs3PnTjZs2JDvfa9cucLQoUOJi4vDw8ODdu3asWfPHsN9Hwh3T7XdleN1MyWTlQeuAjC2Y/H+vYQQQoiHiUa5d57qPtavX09KSgoDBgzg/Pnz9OnTh5MnT+Lm5sayZcvo2rVrWfW1QklMTMTFxYWEhISKmZ907A/4bRT4toOx6w2HZ209y1frTtGkugurXu1QYJK8EEII8SAqzud3sUeSevToYXheq1Ytjh8/zs2bN3F1dZUP3IqkgOX/e8/fBNSEbfn3EkIIIQpWrJyk7OxsLC0tOXr0qNHxqlWrygduRVNAIclTMUkANPZxKe8eCSGEEJVKsYIkS0tL/Pz8il0LSZhBPiNJt1MziUlMB6C+l5M5eiWEEEJUGsVe3fbBBx8wefJkbt68WRb9EaaSTyHJkzmjSDVc7XC0KXGxdSGEEOKhUOxPyu+//56zZ8/i4+ODn59fnj3RDhw4YLLOiVLIp5Bk7lRbAxlFEkIIIe6r2EFS//79y6AbwqQUBRJzgiTnvCNJDbwq4Go8IYQQooIpdpD08ccfl0U/hCllJEFWivr8rpGkkzGJgOQjCSGEEEVR7JwkUQnkTrXZuIC1Oh2q1yucluk2IYQQosiKPZJkYWFR6HJ/WflWAeSTj3T1dhopmTqstRb4uzsUcKIQQgghchU7SPrjjz+Mvs/KyuLgwYMsWrSITz/91GQdE6WQz/L/E9HqVFudao5YaWUAUQghhLifYgdJ/fr1y3Ns0KBBNG7cmGXLljF27FiTdEyUQj6FJGVlmxBCCFE8JhtSCAoKYuPGjaa6nCiNfEaSTl5XgyRJ2hZCCCGKxiRBUlpaGj/88AM1atQwxeVEaSXljCQ55R1JkiBJCCGEKJpiT7fdu5GtoigkJSVhb2/PL7/8YtLOiRK6ZyQpPUvHhTi1JEBDb6mRJIQQQhRFsYOkb7/91ihIsrCwwMPDg6CgIFxdXU3aOVFC9xSSPBubjE6vUMXeimpONmbsmBBCCFF5FDtIGjVqVBl0Q5iMXg/JxiNJhqk2T6dCyzcIIYQQ4o5i5yQtWLCA3377Lc/x3377jUWLFpmkU6IUUuNAnw1owNETgFM5Sdsy1SaEEEIUXbGDpP/973+4u7vnOV6tWjW++OILk3RKlEJuIUkHD9BaAXdqJEnSthBCCFF0xQ6SLl26REBAQJ7jfn5+REVFmaRTohQS81bblpVtQgghRPEVO0iqVq0ahw8fznP80KFDuLm5maRTohSSjJO2b6VkEpuUAUA9TwmShBBCiKIqdpA0ZMgQJkyYwJYtW9DpdOh0OjZv3szrr7/OkCFDyqKPojjuWf5/MmcUqWZVexxtip2nL4QQQjy0iv2p+fnnn3Pp0iUee+wxLC3V0/V6PSNGjJCcpIrgnkKSJ2MkH0kIIYQoiWIHSdbW1ixbtozPP/+cyMhI7OzsaNKkCX5+fmXRP1FcSfkv/5c924QQQojiKfH8S926dalbt64p+yJM4Z5CkiclaVsIIYQokWLnJA0aNIj//e9/eY5PmzaNp59+2iSdEqWQdGd1m16vcPp67kiS1EgSQgghiqPYQdK2bdt44okn8hzv2bMn27dvN0mnRAllZ6jFJAGcfLh8K5XUTB3Wlhb4u9mbt29CCCFEJVPsICk5ORlra+s8x62srEhMTDRJp0QJJV9Xv2qtwb6qYaqtbjVHLLXF/qcWQgghHmrF/uQMDAxk2bJleY6HhobSqFEjk3RKlNDdhSQ1GikiKYQQQpRCsRO3P/zwQwYOHMi5c+fo2rUrAJs2beLXX3/l999/N3kHRTEY8pG8gTvL/xtKPpIQQghRbMUOkp588kn+/PNPvvjiC37//Xfs7Oxo1qwZmzdvxtlZPozNqoBCkjKSJIQQQhRfiUoAPPHEE4bk7du3b7NkyRImTpzIoUOH0Ol0Ju2gKIa7CkmmZ+m4GJcCSI0kIYQQoiRKnM27efNmhg0bho+PDzNnzqR3797s37+/2NeZNWsWAQEB2Nra0qpVK3bs2FFo+yVLltCsWTPs7e3x9vZm9OjRxMfHG15fuHAhGo0mzyM9Pb1U960U7hpJOhubjF6Bqg7WeDjZmLdfQgghRCVUrCDpypUrfP7559SqVYuhQ4fi6upKVlYWK1as4PPPP6dFixbFuvmyZcuYOHEi77//PgcPHqRTp0706tWLqKiofNvv3LmTESNGMHbsWI4dO8Zvv/3Gvn37GDdunFE7Z2dnoqOjjR62trYlvm+lkZgzkuTsw4nonO1IPJ3QaDRm7JQQQghRORU5SOrduzeNGjXi+PHj/PDDD1y7do0ffvihVDefPn06Y8eOZdy4cTRs2JAZM2bg6+vL7Nmz822/Z88e/P39mTBhAgEBAXTs2JEXX3wxzwiWRqPBy8vL6FGa+1Yad40kyco2IYQQonSKHCRt2LCBcePG8emnn/LEE0+g1WpLdePMzEwiIiLo3r270fHu3buza9eufM8JDg7mypUrrFmzBkVRuH79Or///nue4pbJycn4+flRo0YN+vTpw8GDB0t1X4CMjAwSExONHhWOYXWbD6euy55tQgghRGkUOUjasWMHSUlJtG7dmqCgIGbOnMmNGzdKfOO4uDh0Oh2enp5Gxz09PYmJicn3nODgYJYsWcLgwYOxtrbGy8uLKlWqGI1oNWjQgIULF7Jq1SqWLl2Kra0tHTp04MyZMyW+L8DUqVNxcXExPHx9fUv61stGRhJkJqvPnbw4EZ0TJHnLikMhhBCiJIocJLVv3565c+cSHR3Niy++SGhoKNWrV0ev1xMWFkZSUlKJOnBvvoyiKAXm0Bw/fpwJEybw0UcfERERwbp167hw4QLjx483tGnXrh3Dhg2jWbNmdOrUieXLl1OvXr08U4PFuS/A5MmTSUhIMDwuX75c3LdatnILSdo4E59lRVxyBhoN1PN0NG+/hBBCiEqq2Kvb7O3tGTNmDDt37uTIkSO8+eab/O9//6NatWo8+eSTRb6Ou7s7Wq02z+hNbGxsnlGeXFOnTqVDhw68/fbbNG3alB49ejBr1ixCQkKIjo7O9xwLCwvatGljGEkqyX0BbGxscHZ2NnpUKHdtbJubj1Szqj321iWq8iCEEEI89Eq1oVf9+vX56quvuHLlCkuXLi3WudbW1rRq1YqwsDCj42FhYQQHB+d7TmpqKhYWxl3OzY1SFCXfcxRFITIyEm9v7xLft1K4K2nbUETSU/KRhBBCiJIyyTCDVqulf//+9O/fv1jnTZo0ieHDh9O6dWvat2/PnDlziIqKMkyfTZ48matXr7J48WIA+vbty/PPP8/s2bPp0aMH0dHRTJw4kbZt2+Lj4wPAp59+Srt27ahbty6JiYl8//33REZG8uOPPxb5vpXSXYUkc7cjkXwkIYQQouTMOhczePBg4uPjmTJlCtHR0QQGBrJmzRr8/PwAiI6ONqpdNGrUKJKSkpg5cyZvvvkmVapUoWvXrnz55ZeGNrdv3+aFF14gJiYGFxcXWrRowfbt22nbtm2R71sp3b38/5SsbBNCCCFKS6MUNE8lCpWYmIiLiwsJCQkVIz9p2XA4sQp9zy9pvNqftCwdm97sTG0PSdwWQgghchXn87tUOUmiAskZSYqjKmlZOmwsLfB3czBzp4QQQojKS4KkB0XO6rZzGeoUWz1PJ7QWsh2JEEIIUVISJD0I9HrDSNLxJHX0SLYjEUIIIUpHgqQHQWo86LMAOHDLBpCkbSGEEKK0JEh6EOQWknTw4Pj1NAAaeFWAZHIhhBCiEpMg6UGQEyTpHb24GJ8CyHSbEEIIUVoSJD0IcoKkZGsPFAXcHKzxcLIxc6eEEEKIyk2CpAdBTtL2DU1VQEaRhBBCCFOQIOlBkKhuSXI5qwog+UhCCCGEKUiQ9CDIGUk6m6Yu/5eVbUIIIUTpSZD0IMjZ3PZIotRIEkIIIUxFgqQHQc5I0pk0RzQatdq2EEIIIUpHgqTKLjsTUm4AEKNUxd/NATtrrZk7JYQQQlR+EiRVdsnXAdBpLLmFI/VlFEkIIYQwCQmSKrucGkmJlm4oWEg+khBCCGEiEiRVdjlBUoziCsjKNiGEEMJUJEiq7HKSti9lqrWRGnhLjSQhhBDCFCRIquxyCklG612xtbKgZlV7M3dICCGEeDBIkFTZ5YwkXVdcqefphNZCY+YOCSGEEA8GCZIqu5xCkjFKVclHEkIIIUxIgqTKLnckCVfqy55tQgghhMlIkFTZJaqr264rrjKSJIQQQpiQBEmVWUYSZCYBapAkNZKEEEII05EgqTLLmWpLUuywc6yCu6ONmTskhBBCPDgkSKrMcgpJxipVZKpNCCGEMDEJkiqzu5b/y1SbEEIIYVoSJFVmOYUkY6gqQZIQQghhYhIkVWKKYbrNlYay/F8IIYQwKQmSKrGMW1cBtUZSXU9HM/dGCCGEeLBIkFSJZdxUgyTF0QtbK62ZeyOEEEI8WMweJM2aNYuAgABsbW1p1aoVO3bsKLT9kiVLaNasGfb29nh7ezN69Gji4+MNr8+dO5dOnTrh6uqKq6sr3bp1Izw83Ogan3zyCRqNxujh5eVVJu+vLFkkq9Nt9m6+Zu6JEEII8eAxa5C0bNkyJk6cyPvvv8/Bgwfp1KkTvXr1IioqKt/2O3fuZMSIEYwdO5Zjx47x22+/sW/fPsaNG2dos3XrVoYOHcqWLVvYvXs3NWvWpHv37ly9etXoWo0bNyY6OtrwOHLkSJm+V5NTFOzSbwDg7u1v3r4IIYQQDyCzBknTp09n7NixjBs3joYNGzJjxgx8fX2ZPXt2vu337NmDv78/EyZMICAggI4dO/Liiy+yf/9+Q5slS5bw8ssv07x5cxo0aMDcuXPR6/Vs2rTJ6FqWlpZ4eXkZHh4eHmX6Xk0uNR5LsgGoXtPfvH0RQgghHkBmC5IyMzOJiIige/fuRse7d+/Orl278j0nODiYK1eusGbNGhRF4fr16/z+++888cQTBd4nNTWVrKwsqlatanT8zJkz+Pj4EBAQwJAhQzh//nzp31Q5yr6tjozFKc7U93Ezc2+EEEKIB4/ZgqS4uDh0Oh2enp5Gxz09PYmJicn3nODgYJYsWcLgwYOxtrbGy8uLKlWq8MMPPxR4n3fffZfq1avTrVs3w7GgoCAWL17M+vXrmTt3LjExMQQHBxvlNt0rIyODxMREo4c5xV67CMANXKlZ1d6sfRFCCCEeRGZP3NZoNEbfK4qS51iu48ePM2HCBD766CMiIiJYt24dFy5cYPz48fm2/+qrr1i6dCkrV67E1tbWcLxXr14MHDiQJk2a0K1bN1avXg3AokWLCuzn1KlTcXFxMTx8fc2bLB137RIAKTbVsLDI/+clhBBCiJIzW5Dk7u6OVqvNM2oUGxubZ3Qp19SpU+nQoQNvv/02TZs2pUePHsyaNYuQkBCio6ON2n799dd88cUXbNiwgaZNmxbaFwcHB5o0acKZM2cKbDN58mQSEhIMj8uXLxfxnZaN5Dj1/nqH/H9WQgghhCgdswVJ1tbWtGrVirCwMKPjYWFhBAcH53tOamoqFhbGXdZq1fpAiqIYjk2bNo3PPvuMdevW0bp16/v2JSMjgxMnTuDt7V1gGxsbG5ydnY0e5pSbk2RVpbpZ+yGEEEI8qMw63TZp0iTmzZtHSEgIJ06c4I033iAqKsowfTZ58mRGjBhhaN+3b19WrlzJ7NmzOX/+PP/++y8TJkygbdu2+Pj4AOoU2wcffEBISAj+/v7ExMQQExNDcnKy4TpvvfUW27Zt48KFC+zdu5dBgwaRmJjIyJEjy/cHUAralOsAOFeTGklCCCFEWbA0580HDx5MfHw8U6ZMITo6msDAQNasWYOfnx8A0dHRRjWTRo0aRVJSEjNnzuTNN9+kSpUqdO3alS+//NLQZtasWWRmZjJo0CCje3388cd88sknAFy5coWhQ4cSFxeHh4cH7dq1Y8+ePYb7VnQpGdm4ZMeBBVSrHmDu7gghhBAPJI1y9zyVKLLExERcXFxISEgo96m3g1G3qDG/GR6aBHhxO3g3K9f7CyGEEJVVcT6/zb66TRTf6Ws3cSOnBIGTj3k7I4QQQjygJEiqhK5euYSFRkGn0YK9FJIUQgghyoIESZXQzZiLAGTYeICF/BMKIYQQZUE+YSsZRVFIi8/ZrNdZptqEEEKIsiJBUiUTm5SBQ+YNAGxcJUgSQgghyooESZXMyZgkvDS3ANC6SCFJIYQQoqxIkFTJnIpJxDMnSMLJy7ydEUIIIR5gEiRVMidjkvDkpvqN5CQJIYQQZUaCpErmZHQSnprb6jcykiSEEEKUGQmSKpFsnZ6zN5Lx1OSMJEkhSSGEEKLMSJBUiVyMT8EyOxVnTZp6QEaShBBCiDIjQVIlcjIm6U7StrUj2JbvnnFCCCHEw0SCpEpEzUeSlW1CCCFEeZAgqRI5GZNENXKDJG/zdkYIIYR4wEmQVImcup6IlyFpW4IkIYQQoixJkFRJJGdkc/lmmiz/F0IIIcqJBEmVxKmYJAD8rG+rB6SQpBBCCFGmJEiqJHKDpJpWieoBGUkSQgghypQESZXEqRg1OKqmSCFJIYQQojxIkFRJnIhJAhScsuPUAzKSJIQQQpQpCZIqAUVROBWThCtJaPVZ6kEJkoQQQogyJUFSJXA9MYOEtCy8tbfVA/ZuYGlj1j4JIYQQDzoJkiqBkzn5SM2cc/dskxpJQgghRFmTIKkSOJmzsi3QOUU9IEGSEEIIUeYkSKoEcpf/17ZNVg9IPpIQQghR5iRIqgRyR5Jq5OYkSSFJIYQQosxJkFTBZen0nItVR5DcDDWSZCRJCCGEKGsSJFVwF+JSyNTpcbSxxDb9unpQCkkKIYQQZU6CpAoud6qtnqcjmqQY9aCMJAkhhBBlToKkCi53O5KGnvaQHKselJwkIYQQosxJkFTBnYxWR5Kau2YCCmi0YO9u3k4JIYQQDwGzB0mzZs0iICAAW1tbWrVqxY4dOwptv2TJEpo1a4a9vT3e3t6MHj2a+Ph4ozYrVqygUaNG2NjY0KhRI/74449S39dccqfbGjrctfzfwuz/bEIIIcQDz6yftsuWLWPixIm8//77HDx4kE6dOtGrVy+ioqLybb9z505GjBjB2LFjOXbsGL/99hv79u1j3Lhxhja7d+9m8ODBDB8+nEOHDjF8+HCeeeYZ9u7dW+L7mktSehZXb6tVtgNs1GBJCkkKIYQQ5UOjKIpirpsHBQXRsmVLZs+ebTjWsGFD+vfvz9SpU/O0//rrr5k9ezbnzp0zHPvhhx/46quvuHz5MgCDBw8mMTGRtWvXGtr07NkTV1dXli5dWqL75icxMREXFxcSEhJwdnYu3hsvoohLNxk4ezdezrbs6XYe1rwFDfrAkCVlcj8hhBDiQVecz2+zjSRlZmYSERFB9+7djY53796dXbt25XtOcHAwV65cYc2aNSiKwvXr1/n999954oknDG12796d55o9evQwXLMk9zWXEzn5SA28nSDxmnpQkraFEEKIcmG2ICkuLg6dToenp6fRcU9PT2JiYvI9Jzg4mCVLljB48GCsra3x8vKiSpUq/PDDD4Y2MTExhV6zJPcFyMjIIDEx0ehR1nK3I6nv5QSy/F8IIYQoV2bPANZoNEbfK4qS51iu48ePM2HCBD766CMiIiJYt24dFy5cYPz48cW+ZnHuCzB16lRcXFwMD19f3/u+t9LKDZIaeDlBUs5IkhSSFEIIIcqF2YIkd3d3tFptntGb2NjYPKM8uaZOnUqHDh14++23adq0KT169GDWrFmEhIQQHR0NgJeXV6HXLMl9ASZPnkxCQoLhkZsDVVYUReFkTo2k+p7OMpIkhBBClDOzBUnW1ta0atWKsLAwo+NhYWEEBwfne05qaioW9yx/12q1gBpUALRv3z7PNTds2GC4ZknuC2BjY4Ozs7PRoyxFJ6STmJ6NpYWG2tUcIFENAiUnSQghhCgflua8+aRJkxg+fDitW7emffv2zJkzh6ioKMP02eTJk7l69SqLFy8GoG/fvjz//PPMnj2bHj16EB0dzcSJE2nbti0+Pmrw8Prrr/PII4/w5Zdf0q9fP/766y82btzIzp07i3zfiiB3qq2WhwM2+nTISFBfkJEkIYQQolyYNUgaPHgw8fHxTJkyhejoaAIDA1mzZg1+fn4AREdHG9UuGjVqFElJScycOZM333yTKlWq0LVrV7788ktDm+DgYEJDQ/nggw/48MMPqV27NsuWLSMoKKjI960IThqStu+aarOyB5uyHcESQgghhMqsdZIqs7Kuk/R66EH+irzG2z3q80pADCx8AqrWhgkHTH4vIYQQ4mFRKeokicIZr2zLTdqWattCCCFEeZEgqQLK0uk5d0Pdq62+192FJCVIEkIIIcqLBEkV0PkbKWTpFJxsLKlexU6W/wshhBBmIEFSBZRbH6mel5Na4FIKSQohhBDlToKkCujk3flIICNJQgghhBlIkFQBnbo3SJLNbYUQQohyJ0FSBXTq7hpJiiIjSUIIIYQZSJBUwSSkZXH1dhqQs7It7RboMtQXpQSAEEIIUW4kSKpgTl9XR5F8XGxxsbOCpJw92+yqgqWNGXsmhBBCPFwkSKpg7mxHkpuPlBMkySiSEEIIUa7MunebyKtfcx/qVnPESqtRD+SOJEkhSSGEEKJcSZBUwTjbWtGultudA5K0LYR4gOl0OrKysszdDfEAsbKyQqvVmuRaEiRVdFJIUgjxAFIUhZiYGG7fvm3urogHUJUqVfDy8lILMpeCBEkVnYwkCSEeQLkBUrVq1bC3ty/1h5kQoAbfqampxMbGAuDtXbpUFQmSKjopJCmEeMDodDpDgOTm5nb/E4QoBjs7OwBiY2OpVq1aqabeZHVbRScjSUKIB0xuDpK9vb2ZeyIeVLm/W6XNd5MgqSLTZUOKOmQoOUlCiAeNTLGJsmKq3y0JkiqylFhQ9KDRgoO7uXsjhBBCPFQkSKrIcgtJOnqChWmWMwohhBAV2cKFC6lSpYq5uwFIkFSxSSFJIYSokHbt2oVWq6Vnz57m7orZbd26FY1GY7JyDoMHD+b06dMmuVZpSZBUkSXJliRCCFERhYSE8Nprr7Fz506ioqLM1o/KVIgzMzOzSO3s7OyoVq1aGfemaCRIqsgkSBJCiAonJSWF5cuX89JLL9GnTx8WLlxo9PqqVato3bo1tra2uLu7M2DAAMNrGRkZvPPOO/j6+mJjY0PdunWZP38+kP80059//mmUhPzJJ5/QvHlzQkJCqFWrFjY2NiiKwrp16+jYsSNVqlTBzc2NPn36cO7cOaNrXblyhSFDhlC1alUcHBxo3bo1e/fu5eLFi1hYWLB//36j9j/88AN+fn4oilLgz+LixYt06dIFAFdXVzQaDaNGjQLg0Ucf5dVXX2XSpEm4u7vz+OOPAzB9+nSaNGmCg4MDvr6+vPzyyyQnJxuuee/PIfc9//zzz/j7++Pi4sKQIUNISkoqsF+mIkFSRSbL/4UQDwlFUUjNzDbLo7AgID/Lli2jfv361K9fn2HDhrFgwQLDNVavXs2AAQN44oknOHjwIJs2baJ169aGc0eMGEFoaCjff/89J06c4P/+7/9wdHQs1v3Pnj3L8uXLWbFiBZGRkYAauE2aNIl9+/axadMmLCwseOqpp9Dr9QAkJyfTuXNnrl27xqpVqzh06BDvvPMOer0ef39/unXrxoIFC4zus2DBAkaNGlXoSjFfX19WrFgBwKlTp4iOjua7774zvL5o0SIsLS35999/+emnnwCwsLDg+++/5+jRoyxatIjNmzfzzjvvFPqez507x59//sk///zDP//8w7Zt2/jf//5XrJ9bSUgxyYpMCkkKIR4SaVk6Gn203iz3Pj6lB/bWRf84nD9/PsOGDQOgZ8+eJCcns2nTJrp168Z///tfhgwZwqeffmpo36xZMwBOnz7N8uXLCQsLo1u3bgDUqlWr2P3NzMzk559/xsPDw3Bs4MCBefpYrVo1jh8/TmBgIL/++is3btxg3759VK1aFYA6deoY2o8bN47x48czffp0bGxsOHToEJGRkaxcubLQvmi1WsP1qlWrlmckrE6dOnz11VdGxyZOnGh4HhAQwGeffcZLL73ErFmzCryPXq9n4cKFODk5ATB8+HA2bdrEf//730L7V1oyklSRyUiSEEJUKKdOnSI8PJwhQ4YAYGlpyeDBgwkJCQEgMjKSxx57LN9zIyMj0Wq1dO7cuVR98PPzMwqQQB1pefbZZ6lVqxbOzs4EBAQAGPKlIiMjadGihSGguVf//v2xtLTkjz/+ANScqy5duuDv71+qvt49ipZry5YtPP7441SvXh0nJydGjBhBfHw8KSkpBV7H39/fECCBut1I7tYjZUlGkioy2dxWCPGQsLPScnxKD7Pdu6jmz59PdnY21atXNxxTFAUrKytu3bpl2BIj3/sU8hqo01D3Tv3ll5jt4OCQ51jfvn3x9fVl7ty5+Pj4oNfrCQwMNCRL3+/e1tbWDB8+nAULFjBgwAB+/fVXZsyYUeg5RXFvXy9dukTv3r0ZP348n332GVWrVmXnzp2MHTu20CR0Kysro+81Go1hKrEsSZBUUWWmQnqC+lxGkoQQDziNRlOsKS9zyM7OZvHixXzzzTd0797d6LWBAweyZMkSmjZtyqZNmxg9enSe85s0aYJer2fbtm2G6ba7eXh4kJSUREpKiiG4yM05Kkx8fDwnTpzgp59+olOnTgDs3LnTqE3Tpk2ZN28eN2/eLHA0ady4cQQGBjJr1iyysrKMEs4LY21tDah78t3P/v37yc7O5ptvvsHCQp3MWr58eZHuYw4y3VZR5a5ss7QDWxfz9kUIIQT//PMPt27dYuzYsQQGBho9Bg0axPz58/n4449ZunQpH3/8MSdOnODIkSOGnBx/f39GjhzJmDFj+PPPP7lw4QJbt241BAlBQUHY29vz3nvvcfbsWX799dc8K+fy4+rqipubG3PmzOHs2bNs3ryZSZMmGbUZOnQoXl5e9O/fn3///Zfz58+zYsUKdu/ebWjTsGFD2rVrx3/+8x+GDh1639GnXH5+fmg0Gv755x9u3LhhtFLtXrVr1yY7O5sffviB8+fP8/PPP/N///d/RbqPOUiQVFHl5iM5e4PsbySEEGY3f/58unXrhotL3j9cBw4cSGRkJM7Ozvz222+sWrWK5s2b07VrV/bu3WtoN3v2bAYNGsTLL79MgwYNeP755w25OFWrVuWXX35hzZo1NGnShKVLl/LJJ5/ct18WFhaEhoYSERFBYGAgb7zxBtOmTTNqY21tzYYNG6hWrRq9e/emSZMm/O9//0OrNZ5qHDt2LJmZmYwZM6bIP5fq1avz6aef8u677+Lp6cmrr75aYNvmzZszffp0vvzySwIDA1myZAlTp04t8r3Km0Yp7tpHAUBiYiIuLi4kJCTg7Oxs+hsc+R1WjAW/DjB6jemvL4QQZpKens6FCxcICAjA1tbW3N0Rd/nvf/9LaGgoR44cMXdXSqWw37HifH7LSFJFJYUkhRBClJPk5GT27dvHDz/8wIQJE8zdnQrD7EHSrFmzDJFeq1at2LFjR4Ftc4ta3fto3Lixoc2jjz6ab5snnnjC0OaTTz7J87qXVwVLjpbl/0IIIcrJq6++SseOHencuXOeqbbx48fj6OiY72P8+PFm6nH5MOtSgmXLljFx4kRmzZpFhw4d+Omnn+jVqxfHjx+nZs2aedp/9913RhU2s7OzadasGU8//bTh2MqVK432h4mPj8/TBqBx48Zs3LjR8P2987JmJ4UkhRBClJOFCxcWmCQ+ZcoU3nrrrXxfK5N0kwrErEHS9OnTGTt2LOPGjQNgxowZrF+/ntmzZ+ebyOXi4mKUMPfnn39y69Yto6WW9y5tDA0Nxd7ePk+QZGlpWfFGj+4mI0lCCCEqgGrVqlWYDWfLm9mm2zIzM4mIiMhTa6J79+7s2rWrSNfIXWng5+dXaJshQ4bkKWh15swZfHx8CAgIYMiQIZw/f774b6IsSSFJIYQQwqzMNpIUFxeHTqfD09PT6LinpycxMTH3PT86Opq1a9fy66+/FtgmPDyco0ePGnZYzhUUFMTixYupV68e169f5/PPPyc4OJhjx47h5uaW77UyMjLIyMgwfJ+YmHjfPpaYoshIkhBCCGFmZk/cvnd3YUVRCt1xONfChQupUqUK/fv3L7DN/PnzCQwMpG3btkbHe/XqxcCBA2nSpAndunVj9erVgLpbcUGmTp1qmO5zcXHB19f3vn0ssbRbkJ2uPpcgSQghhDALswVJ7u7uaLXaPKNGsbGxeUaX7qUoCiEhIQwfPtxQDv1eqamphIaGGvKdCuPg4ECTJk04c+ZMgW0mT55MQkKC4XH58uX7XrfEckeR7FzBqmgVT4UQQghhWmYLkqytrWnVqhVhYWFGx8PCwggODi703G3btnH27FnGjh1bYJvly5eTkZHBsGHD7tuXjIwMTpw4gbd3wTWJbGxscHZ2NnqUGamRJIQQQpidWVe3TZo0ieHDh9O6dWvat2/PnDlziIqKMtRdmDx5MlevXmXx4sVG582fP5+goCACAwMLvPb8+fPp379/vjlGb731Fn379qVmzZrExsby+eefk5iYyMiRI037BktKgiQhhBDC7MyakzR48GBmzJjBlClTaN68Odu3b2fNmjWG1WrR0dFERUUZnZOQkMCKFSsKHUU6ffo0O3fuLLDNlStXGDp0KPXr12fAgAFYW1uzZ8+eQlfJlSsJkoQQokLbtWsXWq2Wnj17mrsrZrd161Y0Gg23b9822TUvXryIRqMhMjLSZNcsCbOOJAG8/PLLvPzyy/m+ll9hKxcXF1JTUwu9Zr169ShsS7rQ0NBi9bHcJeYESc4SJAkhREUUEhLCa6+9xrx584iKisq3AHJ5yMrKwsrKyiz3fhiYfXWbyIcs/xdCiAorJSWF5cuX89JLL9GnT588f9CvWrWK1q1bY2tri7u7OwMGDDC8lpGRwTvvvIOvry82NjbUrVvXUKYmd9X23f7880+jFd+ffPIJzZs3JyQkhFq1amFjY4OiKKxbt46OHTtSpUoV3Nzc6NOnD+fOnTO61pUrVxgyZAhVq1bFwcGB1q1bs3fvXi5evIiFhQX79+83av/DDz/g5+dX6KDDxYsX6dKlCwCurq5oNBpGjRoFqIusvvrqK2rVqoWdnR3NmjXj999/N5x769YtnnvuOTw8PLCzs6Nu3bosWLAAgICAAABatGiBRqPh0UcfLbAPZcnsI0kiH1JIUgjxsFEUyCp8lqDMWNlDEUrP5Fq2bBn169enfv36DBs2jNdee40PP/wQjUbD6tWrGTBgAO+//z4///wzmZmZhjIzACNGjGD37t18//33NGvWjAsXLhAXF1es7p49e5bly5ezYsUKw5ZaKSkpTJo0iSZNmpCSksJHH33EU089RWRkJBYWFiQnJ9O5c2eqV6/OqlWr8PLy4sCBA+j1evz9/enWrRsLFiygdevWhvssWLDAsGdqQXx9fVmxYgUDBw7k1KlTODs7Y2enrsr+4IMPWLlyJbNnz6Zu3bps376dYcOG4eHhQefOnfnwww85fvw4a9euxd3dnbNnz5KWlgaodQ7btm3Lxo0bady4cYEr2cuaBEkVkYwkCSEeNlmp8IWZ/jB87xpYO9y/XY758+cbVk737NmT5ORkNm3aRLdu3fjvf//LkCFD+PTTTw3tmzVrBqj5ssuXLycsLIxu3boBUKtWrWJ3NzMzk59//hkPDw/DsYEDB+bpY7Vq1Th+/DiBgYH8+uuv3Lhxg3379hm276pTp46h/bhx4xg/fjzTp0/HxsaGQ4cOERkZycqVKwvti1arNVyvWrVqhpGwlJQUpk+fzubNm2nfvr3hve7cuZOffvqJzp07ExUVRYsWLQyBmb+/v+G6ue/Nzc3NrFuIyXRbRaPLhuTr6nPZ3FYIISqUU6dOER4ezpAhQwB1H9DBgwcTEhICQGRkJI899li+50ZGRqLVauncuXOp+uDn52cUIAGcO3eOZ599llq1auHs7GyYrspd/BQZGUmLFi3y7G+aq3///lhaWvLHH38Aas5Vly5djAKX4jh+/Djp6ek8/vjjODo6Gh6LFy82TAO+9NJLhIaG0rx5c955550ib0lWnmQkqaJJuQGKHjQW4OBx//ZCCPEgsLJXR3TMde8imj9/PtnZ2VSvXt1wTFEUrKysuHXrlmGqKT+FvQZgYWGRJ/8nKysrT7t79yIF6Nu3L76+vsydOxcfHx/0ej2BgYFkZmYW6d7W1tYMHz6cBQsWMGDAAH799VdmzJhR6DmF0ev1AKxevdroZwVq3UFQd7+4dOkSq1evZuPGjTz22GO88sorfP311yW+r6lJkFTR5OYjOXqChda8fRFCiPKi0RRrysscsrOzWbx4Md98802ezdkHDhzIkiVLaNq0KZs2bWL06NF5zm/SpAl6vZ5t27YZptvu5uHhQVJSEikpKYZAqChL4OPj4zlx4gQ//fQTnTp1AmDnzp1GbZo2bcq8efO4efNmgaNJ48aNIzAwkFmzZpGVlWWUcF6Y3HwhnU5nONaoUSNsbGyIiooqdOTMw8ODUaNGMWrUKDp16sTbb7/N119/ne81zUGCpIrGkI8ky/+FEKIi+eeff7h16xZjx47FxcXF6LVBgwYxf/58vv32Wx577DFq167NkCFDyM7OZu3atbzzzjv4+/szcuRIxowZY0jcvnTpErGxsTzzzDMEBQVhb2/Pe++9x2uvvUZ4eHi+pXDu5erqipubG3PmzMHb25uoqCjeffddozZDhw7liy++oH///kydOhVvb28OHjyIj4+PIWeoYcOGtGvXjv/85z+MGTPmvqNPufz8/NBoNPzzzz/07t0bOzs7nJyceOutt3jjjTfQ6/V07NiRxMREdu3ahaOjIyNHjuSjjz6iVatWNG7cmIyMDP755x8aNmwIqPlNdnZ2rFu3jho1amBra5vnZ14uFFEiCQkJCqAkJCSY9sKHlinKf30U5dehpr2uEEJUEGlpacrx48eVtLQ0c3elWPr06aP07t0739ciIiIUQImIiFBWrFihNG/eXLG2tlbc3d2VAQMGGNqlpaUpb7zxhuLt7a1YW1srderUUUJCQgyv//HHH0qdOnUUW1tbpU+fPsqcOXOUuz+qP/74Y6VZs2Z57h8WFqY0bNhQsbGxUZo2baps3bpVAZQ//vjD0ObixYvKwIEDFWdnZ8Xe3l5p3bq1snfvXqPrzJ8/XwGU8PDwYv1spkyZonh5eSkajUYZOXKkoiiK8v/t3XtQVOUbB/DvAutyGQRUcJfwQqYgaoypIyhKP5kQzFtSqBGBVqaBWtokXUyqaaIyrXTCdBRtdHKGAIeJ1CQBS0VpBCVFdALNGSXSlIsMiPH8/jB2Wjgs92UXvp+ZnWH3vO/L8/js8TycPbvb0NAgX3zxhXh5eYlarRZXV1eZOXOm5OTkiIjIBx98IKNHjxY7OzsZMGCAzJs3T0pKSvRr7tixQ4YMGSJWVlYSGBjYrniMPcfac/xWiRj5AARqUWVlJZycnFBRUdE93+N2/x5g0zNveSQi6k61tbUoLS2Fp6cnbG1tezoc+o8PP/wQ+/fvR2FhYU+H0inGnmPtOX7z3W3mig0SERGZSHV1NfLy8rBlyxasWrWqp8MxG2ySiIiI+rjY2FgEBAQgMDAQS5cuNdi2fPlyg7fx//fW+IX0vRVfbuugbn+5jYiol+LLbZalvLwclZWVitv69+8PNzc3E0fUuq56uY3vbiMiIqIWubm5mWUjZAp8uY2IiIhIAZskIiLqEbzag7pLVz232CQREZFJqdVqAEBNTU0PR0K9VeNzq/G51lG8JomIiEzK2toazs7OKC8vBwDY29tDpVL1cFTUG4gIampqUF5eDmdnZ1hbd+7rvdgkERGRyWm1WgDQN0pEXcnZ2Vn/HOsMNklERGRyKpUKOp0Obm5uit90T9RRarW602eQGrFJIiKiHmNtbd1lBzSirsYLt4mIiIgUsEkiIiIiUsAmiYiIiEgBr0nqoMYPqmrp+2yIiIjI/DQet9vygZNskjqoqqoKADBkyJAejoSIiIjaq6qqCk5OTkbHqISfC98hDQ0NuH79OhwdHXv1h6BVVlZiyJAhuHbtWqvfltwb9KV8mWvv1ZfyZa69V3flKyKoqqqCu7s7rKyMX3XEM0kdZGVlBQ8Pj54Ow2T69+/fJ3bKRn0pX+bae/WlfJlr79Ud+bZ2BqkRL9wmIiIiUsAmiYiIiEgBmyQySqPRYMOGDdBoND0dikn0pXyZa+/Vl/Jlrr2XOeTLC7eJiIiIFPBMEhEREZECNklERERECtgkERERESlgk0RERESkgE1SH/bRRx9h0qRJcHR0hJubG+bPn4/i4mKjc7Kzs6FSqZrdLl68aKKoOy4+Pr5Z3Fqt1uicnJwcTJgwAba2tnj44Yexbds2E0XbOcOHD1esU0xMjOJ4S6rrsWPHMGfOHLi7u0OlUuHAgQMG20UE8fHxcHd3h52dHR5//HGcP3++1XVTUlLg4+MDjUYDHx8fpKWldVMG7WMs3/r6eqxbtw7jxo2Dg4MD3N3d8fzzz+P69etG19y9e7divWtra7s5G+Naq210dHSzmP38/Fpd1xxr21quSvVRqVT49NNPW1zTXOvalmONue63bJL6sJycHMTExCA3NxdHjhzB/fv3ERwcjLt377Y6t7i4GDdu3NDfRo4caYKIO2/MmDEGcRcWFrY4trS0FLNmzcK0adOQn5+Pt956C6tWrUJKSooJI+6YvLw8gzyPHDkCAHjmmWeMzrOEut69exe+vr7YunWr4vZPPvkEmzZtwtatW5GXlwetVosnnnhC/32LSk6ePImFCxciMjISZ8+eRWRkJMLDw3Hq1KnuSqPNjOVbU1ODM2fOYP369Thz5gxSU1Nx6dIlzJ07t9V1+/fvb1DrGzduwNbWtjtSaLPWagsAISEhBjH/8MMPRtc019q2lmvT2uzatQsqlQphYWFG1zXHurblWGO2+60Q/au8vFwASE5OTotjsrKyBIDcvn3bdIF1kQ0bNoivr2+bx7/xxhvi7e1t8NjLL78sfn5+XRxZ91u9erWMGDFCGhoaFLdbal0BSFpamv5+Q0ODaLVaSUhI0D9WW1srTk5Osm3bthbXCQ8Pl5CQEIPHZs6cKYsWLerymDujab5KTp8+LQDk6tWrLY5JSkoSJyenrg2uiynlGhUVJfPmzWvXOpZQ27bUdd68eTJjxgyjYyyhriLNjzXmvN/yTBLpVVRUAAAGDBjQ6tjx48dDp9MhKCgIWVlZ3R1al7l8+TLc3d3h6emJRYsWoaSkpMWxJ0+eRHBwsMFjM2fOxK+//or6+vruDrXL3Lt3D3v37sXSpUtb/TJmS61ro9LSUpSVlRnUTaPRIDAwECdOnGhxXku1NjbHXFVUVEClUsHZ2dnouOrqagwbNgweHh6YPXs28vPzTRNgJ2VnZ8PNzQ2jRo3CSy+9hPLycqPje0Nt//zzT2RkZOCFF15odawl1LXpscac91s2SQTgwevBa9asQUBAAMaOHdviOJ1Oh+3btyMlJQWpqanw8vJCUFAQjh07ZsJoO2by5Mn45ptvcPjwYezYsQNlZWWYMmUKbt26pTi+rKwMgwcPNnhs8ODBuH//Pm7evGmKkLvEgQMHcOfOHURHR7c4xpLr+l9lZWUAoFi3xm0tzWvvHHNUW1uLuLg4PPvss0a/ENTb2xu7d+9Geno6vv32W9ja2mLq1Km4fPmyCaNtv9DQUOzbtw9Hjx7FZ599hry8PMyYMQN1dXUtzukNtd2zZw8cHR2xYMECo+Msoa5Kxxpz3m9tumwlsmixsbE4d+4cfvnlF6PjvLy84OXlpb/v7++Pa9euYePGjZg+fXp3h9kpoaGh+p/HjRsHf39/jBgxAnv27MGaNWsU5zQ98yL/fkB9a2dkzMnOnTsRGhoKd3f3FsdYcl2VKNWttZp1ZI45qa+vx6JFi9DQ0ICvvvrK6Fg/Pz+DC56nTp2Kxx57DFu2bMGXX37Z3aF22MKFC/U/jx07FhMnTsSwYcOQkZFhtIGw9Nru2rULERERrV5bZAl1NXasMcf9lmeSCCtXrkR6ejqysrLg4eHR7vl+fn5m9ZdKWzk4OGDcuHEtxq7Vapv9RVJeXg4bGxsMHDjQFCF22tWrV5GZmYkXX3yx3XMtsa6N71ZUqlvTvzibzmvvHHNSX1+P8PBwlJaW4siRI0bPIimxsrLCpEmTLK7eOp0Ow4YNMxq3pdf2559/RnFxcYf2YXOra0vHGnPeb9kk9WEigtjYWKSmpuLo0aPw9PTs0Dr5+fnQ6XRdHF33q6urQ1FRUYux+/v7698V1ujHH3/ExIkToVarTRFipyUlJcHNzQ1PPvlku+daYl09PT2h1WoN6nbv3j3k5ORgypQpLc5rqdbG5piLxgbp8uXLyMzM7FADLyIoKCiwuHrfunUL165dMxq3JdcWeHAmeMKECfD19W33XHOpa2vHGrPeb7vsEnCyOCtWrBAnJyfJzs6WGzdu6G81NTX6MXFxcRIZGam/v3nzZklLS5NLly7Jb7/9JnFxcQJAUlJSeiKFdlm7dq1kZ2dLSUmJ5ObmyuzZs8XR0VGuXLkiIs1zLSkpEXt7e3nttdfkwoULsnPnTlGr1fLdd9/1VArt8s8//8jQoUNl3bp1zbZZcl2rqqokPz9f8vPzBYBs2rRJ8vPz9e/mSkhIECcnJ0lNTZXCwkJZvHix6HQ6qays1K8RGRkpcXFx+vvHjx8Xa2trSUhIkKKiIklISBAbGxvJzc01eX5NGcu3vr5e5s6dKx4eHlJQUGCwH9fV1enXaJpvfHy8HDp0SH7//XfJz8+XJUuWiI2NjZw6daonUtQzlmtVVZWsXbtWTpw4IaWlpZKVlSX+/v7y0EMPWWRtW3sei4hUVFSIvb29JCYmKq5hKXVty7HGXPdbNkl9GADFW1JSkn5MVFSUBAYG6u9//PHHMmLECLG1tRUXFxcJCAiQjIwM0wffAQsXLhSdTidqtVrc3d1lwYIFcv78ef32prmKiGRnZ8v48eOlX79+Mnz48Bb/szJHhw8fFgBSXFzcbJsl17Xx4wqa3qKiokTkwduJN2zYIFqtVjQajUyfPl0KCwsN1ggMDNSPb5ScnCxeXl6iVqvF29vbbBpEY/mWlpa2uB9nZWXp12ia76uvvipDhw6Vfv36iaurqwQHB8uJEydMn1wTxnKtqamR4OBgcXV1FbVaLUOHDpWoqCj5448/DNawlNq29jwWEfn666/Fzs5O7ty5o7iGpdS1Lccac91vVf8mQERERET/wWuSiIiIiBSwSSIiIiJSwCaJiIiISAGbJCIiIiIFbJKIiIiIFLBJIiIiIlLAJomIiIhIAZskIqJOUKlUOHDgQE+HQUTdgE0SEVms6OhoqFSqZreQkJCeDo2IegGbng6AiKgzQkJCkJSUZPCYRqPpoWiIqDfhmSQismgajQZardbg5uLiAuDBS2GJiYkIDQ2FnZ0dPD09kZycbDC/sLAQM2bMgJ2dHQYOHIhly5ahurraYMyuXbswZswYaDQa6HQ6xMbGGmy/efMmnnrqKdjb22PkyJFIT0/Xb7t9+zYiIiLg6uoKOzs7jBw5sllTR0TmiU0SEfVq69evR1hYGM6ePYvnnnsOixcvRlFREQCgpqYGISEhcHFxQV5eHpKTk5GZmWnQBCUmJiImJgbLli1DYWEh0tPT8cgjjxj8jvfeew/h4eE4d+4cZs2ahYiICPz999/633/hwgUcPHgQRUVFSExMxKBBg0z3D0BEHdelX5dLRGRCUVFRYm1tLQ4ODga3999/X0QefPv48uXLDeZMnjxZVqxYISIi27dvFxcXF6murtZvz8jIECsrKykrKxMREXd3d3n77bdbjAGAvPPOO/r71dXVolKp5ODBgyIiMmfOHFmyZEnXJExEJsVrkojIov3vf/9DYmKiwWMDBgzQ/+zv72+wzd/fHwUFBQCAoqIi+Pr6wsHBQb996tSpaGhoQHFxMVQqFa5fv46goCCjMTz66KP6nx0cHODo6Ijy8nIAwIoVKxAWFoYzZ84gODgY8+fPx5QpUzqUKxGZFpskIrJoDg4OzV7+ao1KpQIAiIj+Z6UxdnZ2bVpPrVY3m9vQ0AAACA0NxdWrV5GRkYHMzEwEBQUhJiYGGzdubFfMRGR6vCaJiHq13NzcZve9vb0BAD4+PigoKMDdu3f1248fPw4rKyuMGjUKjo6OGD58OH766adOxeDq6oro6Gjs3bsXn3/+ObZv396p9YjINHgmiYgsWl1dHcrKygwes7Gx0V8cnZycjIkTJyIgIAD79u3D6dOnsXPnTgBAREQENmzYgKioKMTHx+Ovv/7CypUrERkZicGDBwMA4uPjsXz5cri5uSE0NBRVVVU4fvw4Vq5c2ab43n33XUyYMAFjxoxBXV0dvv/+e4wePboL/wWIqLuwSSIii3bo0CHodDqDx7y8vHDx4kUAD955tn//frzyyivQarXYt28ffHx8AAD29vY4fPgwVq9ejUmTJsHe3h5hYWHYtGmTfq2oqCjU1tZi8+bNeP311zFo0CA8/fTTbY6vX79+ePPNN3HlyhXY2dlh2rRp2L9/fxdkTkTdTSUi0tNBEBF1B5VKhbS0NMyfP7+nQyEiC8RrkoiIiIgUsEkiIiIiUsBrkoio1+LVBETUGTyTRERERKSATRIRERGRAjZJRERERArYJBEREREpYJNEREREpIBNEhEREZECNklERERECtgkERERESlgk0RERESk4P+o2cWHlQB+iAAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-11T09:36:04.355791Z",
     "start_time": "2024-12-11T09:36:04.352452Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "6c3635495afdb72e",
   "outputs": [],
   "execution_count": null
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
